def perform_analysis_and_visualization(data_store):
if True:
analog_ids = param_filter_query(data_store,sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
analog_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
#find neuron with preference closet to 0
NeuronAnnotationsToPerNeuronValues(data_store,ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Exc_L4')
l4_exc_phase = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentPhase', sheet_name = 'V1_Exc_L4')
l4_exc = analog_ids[numpy.argmin([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_exc_or[0].get_value_by_id(analog_ids),l4_exc_phase[0].get_value_by_id(analog_ids))])]
l4_inh_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Inh_L4')
l4_inh_phase = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentPhase', sheet_name = 'V1_Inh_L4')
l4_inh = analog_ids_inh[numpy.argmin([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_inh_or[0].get_value_by_id(analog_ids_inh),l4_inh_phase[0].get_value_by_id(analog_ids_inh))])]
l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(numpy.array([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_exc_or[0].values,l4_exc_phase[0].values)]) < 0.1)[0]]
print "Prefered orientation of plotted exc neurons:"
print 'index ' + str(l4_exc)
print "Prefered phase of plotted exc neurons:"
print l4_exc_phase[0].get_value_by_id(l4_exc)
print "Prefered orientation of plotted inh neurons:"
print l4_inh_phase[0].get_value_by_id(l4_inh)
print 'index ' + str(l4_inh)
print "Prefered phase of plotted inh neurons:"
print l4_exc_phase[0].get_value_by_id(l4_exc)
#dsv = param_filter_query(data_store,sheet_name='V1_Exc_L4')
#TrialAveragedFiringRate(param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4'],st_name="FullfieldDriftingSinusoidalGrating"),ParameterSet({})).analyse()
dsv = param_filter_query(data_store,st_name=['InternalStimulus'])
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[0], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name='SSExcAnalog.png').plot()
RetinalInputMovie(dsv, ParameterSet({}), fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name='video').plot()
def trial_averaged_raster(sheet,
folder,
stimulus,
parameter,
opposite=False,
box=None,
radius=None,
addon=""):
print inspect.stack()[0][3]
print "folder: ", folder
print "sheet: ", sheet
data_store = PickledDataStore(load=True,
parameters=ParameterSet({
'root_directory':
folder,
'store_stimuli':
False
}),
replace=True)
data_store.print_content(full_recordings=False)
spike_ids = param_filter_query(
data_store,
sheet_name=sheet).get_segments()[0].get_stored_spike_train_ids()
if spike_ids == None:
print "No spikes recorded.\n"
return
print "Recorded neurons:", len(spike_ids)
if sheet == 'V1_Exc_L4' or sheet == 'V1_Inh_L4':
NeuronAnnotationsToPerNeuronValues(data_store,
ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name=sheet)[0]
if opposite:
addon = addon + "_opposite"
l4_exc_or_many = numpy.array(spike_ids)[numpy.nonzero(
numpy.array([
circular_dist(l4_exc_or.get_value_by_id(i), numpy.pi /
2, numpy.pi) for i in spike_ids
]) < .1)[0]]
else:
addon = addon + "_same"
l4_exc_or_many = numpy.array(spike_ids)[numpy.nonzero(
numpy.array([
circular_dist(l4_exc_or.get_value_by_id(i), 0, numpy.pi)
for i in spike_ids
]) < .1)[0]]
spike_ids = list(l4_exc_or_many)
if radius or box:
sheet_ids = data_store.get_sheet_indexes(sheet_name=sheet,
neuron_ids=spike_ids)
positions = data_store.get_neuron_postions()[sheet]
if box:
ids1 = select_ids_by_position(positions, sheet_ids, box=box)
if radius:
ids1 = select_ids_by_position(positions, sheet_ids, radius=radius)
spike_ids = data_store.get_sheet_ids(sheet_name=sheet, indexes=ids1)
print "Selected neurons:", len(spike_ids)
if len(spike_ids) < 1:
return
dsv = param_filter_query(data_store, sheet_name=sheet, st_name=stimulus)
dist = box if not radius else radius
# Raster + Histogram
RasterPlot(dsv,
ParameterSet({
'sheet_name': sheet,
'neurons': list(spike_ids),
'trial_averaged_histogram': True,
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (100, 50)
},
plot_file_name=folder + "/HistRaster_" + parameter + "_" +
str(sheet) + "_radius" + str(dist) + "_" + addon + ".svg").plot(
{'SpikeRasterPlot.group_trials': True})
def trial_averaged_Vm(sheet,
folder,
stimulus,
parameter,
opposite=False,
box=None,
radius=None,
addon=""):
print inspect.stack()[0][3]
print "folder: ", folder
print "sheet: ", sheet
data_store = PickledDataStore(load=True,
parameters=ParameterSet({
'root_directory':
folder,
'store_stimuli':
False
}),
replace=True)
data_store.print_content(full_recordings=False)
analog_ids = param_filter_query(
data_store, sheet_name=sheet).get_segments()[0].get_stored_vm_ids()
if analog_ids == None:
print "No Vm recorded.\n"
return
print "Recorded neurons:", len(analog_ids)
if sheet == 'V1_Exc_L4' or sheet == 'V1_Inh_L4':
NeuronAnnotationsToPerNeuronValues(data_store,
ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name=sheet)[0]
if opposite:
addon = addon + "_opposite"
l4_exc_or_many = numpy.array(analog_ids)[numpy.nonzero(
numpy.array([
circular_dist(l4_exc_or.get_value_by_id(i), numpy.pi /
2, numpy.pi) for i in analog_ids
]) < .1)[0]]
else:
addon = addon + "_same"
l4_exc_or_many = numpy.array(analog_ids)[numpy.nonzero(
numpy.array([
circular_dist(l4_exc_or.get_value_by_id(i), 0, numpy.pi)
for i in analog_ids
]) < .1)[0]]
analog_ids = list(l4_exc_or_many)
if radius or box:
sheet_ids = data_store.get_sheet_indexes(sheet_name=sheet,
neuron_ids=analog_ids)
positions = data_store.get_neuron_postions()[sheet]
if box:
ids1 = select_ids_by_position(positions, sheet_ids, box=box)
if radius:
ids1 = select_ids_by_position(positions, sheet_ids, radius=radius)
analog_ids = data_store.get_sheet_ids(sheet_name=sheet, indexes=ids1)
print "Selected neurons:", len(analog_ids)
if len(analog_ids) < 1:
return
dsv = param_filter_query(data_store, sheet_name=sheet, st_name=stimulus)
dist = box if not radius else radius
for n in analog_ids:
VmPlot(
dsv,
ParameterSet({
'neuron': n,
'sheet_name': sheet,
'spontaneous': True,
}),
fig_param={
'dpi': 300,
'figsize': (40, 5)
},
# plot_file_name=folder+"/Vm_"+parameter+"_"+str(sheet)+"_"+str(dist)+"_"+str(n)+"_"+addon+".png"
plot_file_name=folder + "/Vm_" + parameter + "_" + str(sheet) +
"_radius" + str(dist) + "_" + str(n) + "_" + addon + ".svg"
).plot({
# '*.y_lim':(0,60),
# '*.x_scale':'log', '*.x_scale_base':2,
# '*.y_ticks':[5, 10, 25, 50, 60],
# # '*.y_scale':'linear',
# '*.y_scale':'log', '*.y_scale_base':2,
# '*.fontsize':24
})
def perform_analysis_and_visualization_bar(data_store):
NeuronAnnotationsToPerNeuronValues(data_store, ParameterSet({})).analyse()
analog_ids = param_filter_query(
data_store, sheet_name="X_ON").get_segments()[0].get_stored_esyn_ids()
spike_ids = param_filter_query(
data_store,
sheet_name="X_ON").get_segments()[0].get_stored_spike_train_ids()
OverviewPlot(data_store,
ParameterSet({
'sheet_name': 'X_ON',
'neuron': analog_ids[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSX_ONAnalog1.png').plot()
OverviewPlot(data_store,
ParameterSet({
'sheet_name': 'X_ON',
'neuron': analog_ids[1],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSX_ONAnalog2.png').plot()
OverviewPlot(data_store,
ParameterSet({
'sheet_name': 'X_ON',
'neuron': analog_ids[2],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSX_ONAnalog3.png').plot()
RasterPlot(data_store,
ParameterSet({
'sheet_name': 'X_ON',
'neurons': spike_ids,
'trial_averaged_histogram': False,
'spontaneous': False
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='ONRaster.png').plot(
{'SpikeRasterPlot.group_trials': True})
RasterPlot(data_store,
ParameterSet({
'sheet_name': 'X_OFF',
'neurons': lgn_off_ids,
'trial_averaged_histogram': False,
'spontaneous': False
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='ONRaster.png').plot(
{'SpikeRasterPlot.group_trials': True})
dsv = param_filter_query(data_store,
st_name=['FlashedBar'],
st_relative_luminance=1)
RasterPlot(dsv,
ParameterSet({
'sheet_name': 'X_ON',
'neurons': lgn_on_ids,
'trial_averaged_histogram': True,
'spontaneous': False
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='ONBar_ONRaster.png').plot(
{'SpikeRasterPlot.group_trials': True})
dsv = param_filter_query(data_store,
st_name=['FlashedBar'],
st_relative_luminance=1)
RasterPlot(dsv,
ParameterSet({
'sheet_name': 'X_OFF',
'neurons': lgn_off_ids,
'trial_averaged_histogram': True,
'spontaneous': False
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='ONBar_OFFRaster.png').plot(
{'SpikeRasterPlot.group_trials': True})
dsv = param_filter_query(data_store,
st_name=['FlashedBar'],
st_relative_luminance=0)
RasterPlot(dsv,
ParameterSet({
'sheet_name': 'X_ON',
'neurons': lgn_on_ids,
'trial_averaged_histogram': True,
'spontaneous': False
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='OFFBar_ONRaster.png').plot(
{'SpikeRasterPlot.group_trials': True})
dsv = param_filter_query(data_store,
st_name=['FlashedBar'],
st_relative_luminance=0)
RasterPlot(dsv,
ParameterSet({
'sheet_name': 'X_OFF',
'neurons': lgn_off_ids,
'trial_averaged_histogram': True,
'spontaneous': False
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='OFFBar_OFFRaster.png').plot(
{'SpikeRasterPlot.group_trials': True})
def perform_analysis_and_visualization_small(data_store):
dsv = queries.param_filter_query(data_store, st_name='FlashedBar')
for ads in dsv.get_analysis_result():
sid = MozaikParametrized.idd(ads.stimulus_id)
sid.x = 0
ads.stimulus_id = str(sid)
for seg in dsv.get_segments():
sid = MozaikParametrized.idd(seg.annotations['stimulus'])
sid.x = 0
seg.annotations['stimulus'] = str(sid)
PSTH(
param_filter_query(data_store,
sheet_name=['X_ON', 'X_OFF'],
st_name='FlashedBar'),
ParameterSet({'bin_length': 10})).analyse()
NeuronAnnotationsToPerNeuronValues(data_store, ParameterSet({})).analyse()
dsv = param_filter_query(data_store, sheet_name=['X_ON', 'X_OFF'])
ActionPotentialRemoval(dsv, ParameterSet({'window_length': 5.0})).analyse()
TrialVariability(
data_store,
ParameterSet({
'vm': False,
'cond_exc': False,
'cond_inh': False
})).analyse()
TrialMean(
data_store,
ParameterSet({
'vm': False,
'cond_exc': False,
'cond_inh': False
})).analyse()
analog_ids_on = param_filter_query(
data_store, sheet_name="X_ON").get_segments()[0].get_stored_esyn_ids()
analog_ids_off = param_filter_query(
data_store,
sheet_name="X_OFF").get_segments()[0].get_stored_esyn_ids()
lgn_on_ids = param_filter_query(
data_store,
sheet_name="X_ON").get_segments()[0].get_stored_spike_train_ids()
lgn_off_ids = param_filter_query(
data_store,
sheet_name="X_OFF").get_segments()[0].get_stored_spike_train_ids()
OverviewPlot(data_store,
ParameterSet({
'sheet_name': 'X_ON',
'neuron': analog_ids_on[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='ONAnalog.png').plot()
OverviewPlot(data_store,
ParameterSet({
'sheet_name': 'X_OFF',
'neuron': analog_ids_off[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='OFFAnalog.png').plot()
dsv = queries.param_filter_query(data_store,
st_name='FlashedBar',
y_axis_name=[
'psth (bin=10) trial-to-trial mean',
'Vm (no AP) trial-to-trial mean'
],
sheet_name='X_ON',
analysis_algorithm=['TrialMean', 'PSTH'],
st_relative_luminance=1)
PlotTemporalTuningCurve(
dsv,
ParameterSet({
'parameter_name': 'y',
'neurons': list(lgn_on_ids),
'sheet_name': 'X_ON',
'mean': False
}),
fig_param={
'dpi': 100,
'figsize': (20, 10)
},
plot_file_name='AnalogSignalEvolutionOnBar_X_ON.png').plot({
'*.y_label':
'offset',
'*.interpolation':
'nearest'
})
dsv = queries.param_filter_query(data_store,
st_name='FlashedBar',
y_axis_name=[
'psth (bin=10) trial-to-trial mean',
'Vm (no AP) trial-to-trial mean'
],
sheet_name='X_OFF',
analysis_algorithm=['TrialMean', 'PSTH'],
st_relative_luminance=1)
PlotTemporalTuningCurve(
dsv,
ParameterSet({
'parameter_name': 'y',
'neurons': list(lgn_off_ids),
'sheet_name': 'X_OFF',
'mean': False
}),
fig_param={
'dpi': 100,
'figsize': (20, 10)
},
plot_file_name='AnalogSignalEvolutionOnBar_X_OFF.png').plot({
'*.y_label':
'offset',
'*.interpolation':
'nearest'
})
dsv = queries.param_filter_query(data_store,
st_name='FlashedBar',
y_axis_name=[
'psth (bin=10) trial-to-trial mean',
'Vm (no AP) trial-to-trial mean'
],
sheet_name='X_ON',
analysis_algorithm=['TrialMean', 'PSTH'],
st_relative_luminance=0)
PlotTemporalTuningCurve(
dsv,
ParameterSet({
'parameter_name': 'y',
'neurons': list(lgn_on_ids),
'sheet_name': 'X_ON',
'mean': False
}),
fig_param={
'dpi': 100,
'figsize': (20, 10)
},
plot_file_name='AnalogSignalEvolutionOffBar_X_ON.png').plot({
'*.y_label':
'offset',
'*.interpolation':
'nearest'
})
dsv = queries.param_filter_query(data_store,
st_name='FlashedBar',
y_axis_name=[
'psth (bin=10) trial-to-trial mean',
'Vm (no AP) trial-to-trial mean'
],
sheet_name='X_OFF',
analysis_algorithm=['TrialMean', 'PSTH'],
st_relative_luminance=0)
PlotTemporalTuningCurve(
dsv,
ParameterSet({
'parameter_name': 'y',
'neurons': list(lgn_off_ids),
'sheet_name': 'X_OFF',
'mean': False
}),
fig_param={
'dpi': 100,
'figsize': (20, 10)
},
plot_file_name='AnalogSignalEvolutionOffBar_X_OFF.png').plot({
'*.y_label':
'offset',
'*.interpolation':
'nearest'
})
def perform_analysis_and_visualization(data_store):
NeuronAnnotationsToPerNeuronValues(data_store, ParameterSet({})).analyse()
analog_ids = param_filter_query(
data_store, sheet_name="X_ON").get_segments()[0].get_stored_esyn_ids()
spike_ids = param_filter_query(
data_store,
sheet_name="X_ON").get_segments()[0].get_stored_spike_train_ids()
spike_ids_off = param_filter_query(
data_store,
sheet_name="X_OFF").get_segments()[0].get_stored_spike_train_ids()
OverviewPlot(data_store,
ParameterSet({
'sheet_name': 'X_ON',
'neuron': analog_ids[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSX_ONAnalog1.png').plot()
OverviewPlot(data_store,
ParameterSet({
'sheet_name': 'X_ON',
'neuron': analog_ids[1],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSX_ONAnalog2.png').plot()
OverviewPlot(data_store,
ParameterSet({
'sheet_name': 'X_ON',
'neuron': analog_ids[2],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSX_ONAnalog3.png').plot()
RasterPlot(data_store,
ParameterSet({
'sheet_name': 'X_ON',
'neurons': spike_ids,
'trial_averaged_histogram': False,
'spontaneous': False
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSExcRasterL4.png').plot(
{'SpikeRasterPlot.group_trials': True})
def perform_analysis_and_visualization(data_store):
if True:
analog_ids = param_filter_query(
data_store,
sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
analog_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
#find neuron with preference closet to 0
NeuronAnnotationsToPerNeuronValues(data_store,
ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Exc_L4')
l4_exc_phase = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentPhase',
sheet_name='V1_Exc_L4')
l4_exc = analog_ids[numpy.argmin([
circular_dist(o, numpy.pi / 2, numpy.pi)
for (o, p) in zip(l4_exc_or[0].get_value_by_id(analog_ids),
l4_exc_phase[0].get_value_by_id(analog_ids))
])]
l4_inh_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Inh_L4')
l4_inh_phase = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentPhase',
sheet_name='V1_Inh_L4')
l4_inh = analog_ids_inh[numpy.argmin([
circular_dist(o, numpy.pi / 2, numpy.pi)
for (o, p) in zip(l4_inh_or[0].get_value_by_id(analog_ids_inh),
l4_inh_phase[0].get_value_by_id(analog_ids_inh))
])]
l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(
numpy.array([
circular_dist(o, numpy.pi / 2, numpy.pi)
for (o, p) in zip(l4_exc_or[0].values, l4_exc_phase[0].values)
]) < 0.1)[0]]
print "Prefered orientation of plotted exc neurons:"
print 'index ' + str(l4_exc)
print "Prefered phase of plotted exc neurons:"
print l4_exc_phase[0].get_value_by_id(l4_exc)
print "Prefered orientation of plotted inh neurons:"
print l4_inh_phase[0].get_value_by_id(l4_inh)
print 'index ' + str(l4_inh)
print "Prefered phase of plotted inh neurons:"
print l4_exc_phase[0].get_value_by_id(l4_exc)
if False:
#data_store.remove_ads_from_datastore()
dsv = param_filter_query(data_store, sheet_name='V1_Exc_L4')
ActionPotentialRemoval(dsv, ParameterSet({'window_length':
10.0})).analyse()
TrialAveragedFiringRate(
param_filter_query(data_store,
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'],
st_name="FullfieldDriftingSinusoidalGrating"),
ParameterSet({})).analyse()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
analysis_algorithm='TrialAveragedFiringRate',
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'])
GaussianTuningCurveFit(dsv,
ParameterSet({'parameter_name':
'orientation'})).analyse()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'])
Analog_F0andF1(dsv, ParameterSet({})).analyse()
data_store.save()
Analog_MeanSTDAndFanoFactor(data_store, ParameterSet({})).analyse()
#data_store.save()
PSTH(param_filter_query(data_store, sheet_name='V1_Exc_L4'),
ParameterSet({'bin_length': 2.0})).analyse()
GSTA(param_filter_query(data_store,
sheet_name='V1_Exc_L4',
st_name='InternalStimulus',
direct_stimulation_name='None'),
ParameterSet({
'neurons': [l4_exc],
'length': 250.0
}),
tags=['GSTA']).analyse()
GSTA(param_filter_query(data_store,
sheet_name='V1_Exc_L4',
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=[0, numpy.pi / 2]),
ParameterSet({
'neurons': [l4_exc],
'length': 250.0
}),
tags=['GSTA']).analyse()
GSTA(param_filter_query(data_store,
sheet_name='V1_Inh_L4',
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=[0, numpy.pi / 2]),
ParameterSet({
'neurons': [l4_inh],
'length': 250.0
}),
tags=['GSTA']).analyse()
GSTA(param_filter_query(data_store,
sheet_name='V1_Exc_L4',
st_name='NaturalImageWithEyeMovement'),
ParameterSet({
'neurons': [l4_exc],
'length': 250.0
}),
tags=['GSTA']).analyse()
#data_store.print_content(full_ADS=True)
#Precision(param_filter_query(data_store,sheet_name='V1_Exc_L4'),ParameterSet({'neurons' : [l4_exc], 'bin_length' : 10.0 })).analyse()
#dsv = param_filter_query(data_store,st_name='NaturalImageWithEyeMovement',sheet_name='V1_Exc_L4',analysis_algorithm='ActionPotentialRemoval')
#TrialVariability(dsv,ParameterSet({'vm': False, 'cond_exc': False, 'cond_inh': False})).analyse()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name='V1_Exc_L4',
st_contrast=100,
analysis_algorithm='ActionPotentialRemoval')
TrialVariability(
dsv,
ParameterSet({
'vm': False,
'cond_exc': False,
'cond_inh': False
})).analyse()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
analysis_algorithm='TrialAveragedFiringRate',
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'])
PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage(
dsv, ParameterSet({'parameter_name': 'orientation'})).analyse()
pnv = param_filter_query(
data_store,
st_name='InternalStimulus',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_MeanSTDAndFanoFactor'],
value_name='Mean(ECond)',
st_direct_stimulation_name='None').get_analysis_result()[0]
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_F0andF1'],
value_name='F0_Exc_Cond')
SubtractPNVfromPNVS(pnv, dsv, ParameterSet({})).analyse()
pnv = param_filter_query(
data_store,
st_name='InternalStimulus',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_MeanSTDAndFanoFactor'],
value_name='Mean(ICond)',
st_direct_stimulation_name='None').get_analysis_result()[0]
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_F0andF1'],
value_name='F0_Inh_Cond')
SubtractPNVfromPNVS(pnv, dsv, ParameterSet({})).analyse()
pnv = param_filter_query(
data_store,
st_name='InternalStimulus',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_MeanSTDAndFanoFactor'],
value_name='Mean(VM)',
st_direct_stimulation_name='None').get_analysis_result()[0]
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_F0andF1'],
value_name='F0_Vm')
SubtractPNVfromPNVS(pnv, dsv, ParameterSet({})).analyse()
TrialToTrialCrossCorrelationOfAnalogSignalList(
param_filter_query(data_store,
sheet_name='V1_Exc_L4',
st_name="NaturalImageWithEyeMovement",
analysis_algorithm='ActionPotentialRemoval'),
ParameterSet({'neurons': list(analog_ids)})).analyse()
TrialToTrialCrossCorrelationOfAnalogSignalList(
param_filter_query(data_store,
sheet_name='V1_Exc_L4',
st_name="NaturalImageWithEyeMovement",
analysis_algorithm='PSTH'),
ParameterSet({'neurons': list(analog_ids)})).analyse()
data_store.save()
dsv = param_filter_query(
data_store,
analysis_algorithm='TrialToTrialCrossCorrelationOfAnalogSignalList')
dsv.print_content(full_ADS=True)
if True: # PLOTTING
#TrialCrossCorrelationAnalysis(data_store,ParameterSet({'neurons' : list(analog_ids)}),fig_param={"dpi" : 100,"figsize": (25,12)},plot_file_name="trial-to-trial-cross-correlation.png").plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[0]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR1.png').plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[1]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR2.png').plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[2]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR3.png').plot()
dsv = param_filter_query(data_store, st_name=['InternalStimulus'])
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSExcAnalog.png').plot()
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Inh_L4',
'neuron': analog_ids_inh[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSInhAnalog.png').plot()
#dsv = param_filter_query(data_store,st_orientation=[0,numpy.pi/2],st_name='FullfieldDriftingSinusoidalGrating',st_trial=0)
#VmPlot(dsv,ParameterSet({'neuron' : l4_exc,'sheet_name' : 'V1_Exc_L4'})).plot({'Vm_plot.y_lim' : (-67,-56)})
#dsv = param_filter_query(data_store,st_orientation=[0,numpy.pi/2],st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm='ActionPotentialRemoval',st_trial=0)
#AnalogSignalListPlot(dsv,ParameterSet({'neurons' : [l4_exc],'sheet_name' : 'V1_Exc_L4'})).plot({'AnalogSignalPlot.y_lim' : (-67,-56)})
#RetinalInputMovie(data_store,ParameterSet({'frame_rate' : 10})).plot()
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4'],value_name='LGNAfferentOrientation')
#PerNeuronValuePlot(dsv,ParameterSet({}),plot_file_name="LGNAfferentOrientation.png").plot()
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4'],value_name='orientation preference',analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage',st_contrast=100)
#PerNeuronValuePlot(dsv,ParameterSet({})).plot()
#dsv = param_filter_query(data_store,value_name=['orientation HWHH'],sheet_name=['V1_Exc_L4','V1_Inh_L4'])
#PerNeuronValueScatterPlot(dsv,ParameterSet({})).plot({ 'ScatterPlot.x_lim' : (0,90), 'ScatterPlot.y_lim' : (0,90), 'ScatterPlot.identity_line' : True})
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[0]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR1.png').plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[1]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR2.png').plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[2]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR3.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[0],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison1.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[1],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison2.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[2],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison3.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[3],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison4.png').plot()
#TrialToTrialVariabilityComparison(data_store,ParameterSet({}),plot_file_name='TtTVar.png').plot()
#ConductanceAndVmTuningSummary(data_store,ParameterSet({'many' : True,'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (25,16)},plot_file_name='Cond&VMTuning.png').plot()
#dsv = param_filter_query(data_store,st_name='NaturalImageWithEyeMovement')
#KremkowOverviewFigure(dsv,ParameterSet({'neuron' : l4_exc,'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name='NMOverview.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : l4_exc,'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name='StimulusResponseComparison.png').plot()
#OrientationTuningSummary(data_store,ParameterSet({}),fig_param={'dpi' : 100,'figsize': (20,12)},plot_file_name='OrientationTuningSummary.png').plot()
if True:
#OverviewPlot(data_store,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="All.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',st_orientation=[0,numpy.pi/2])
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="Exc1.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_contrast=100)
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': l4_exc,
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc2.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc3.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[1],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc4.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[2],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc5.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[3],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc6.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[4],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc7.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[5],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc8.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[6],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc9.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[7],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc10.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Inh_L4',
'neuron': l4_inh,
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="Inh.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(
dsv,
ParameterSet({
'sheet_name':
'X_ON',
'neuron':
sorted(
param_filter_query(data_store,
sheet_name="X_ON").get_segments()
[0].get_stored_esyn_ids())[0],
'sheet_activity': {},
'spontaneous':
True
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="LGN0On.png").plot()
OverviewPlot(
dsv,
ParameterSet({
'sheet_name':
'X_OFF',
'neuron':
sorted(
param_filter_query(data_store,
sheet_name="X_OFF").get_segments()
[0].get_stored_esyn_ids())[0],
'sheet_activity': {},
'spontaneous':
True
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="LGN0Off.png").plot()
OverviewPlot(
dsv,
ParameterSet({
'sheet_name':
'X_ON',
'neuron':
sorted(
param_filter_query(data_store,
sheet_name="X_ON").get_segments()
[0].get_stored_esyn_ids())[1],
'sheet_activity': {},
'spontaneous':
True
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="LGN1On.png").plot()
OverviewPlot(
dsv,
ParameterSet({
'sheet_name':
'X_OFF',
'neuron':
sorted(
param_filter_query(data_store,
sheet_name="X_OFF").get_segments()
[0].get_stored_esyn_ids())[1],
'sheet_activity': {},
'spontaneous':
True
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="LGN1Off.png").plot()
#TrialAveragedFiringRate(param_filter_query(data_store,sheet_name=['X_ON'],st_name="DriftingSinusoidalGratingDisk"),ParameterSet({})).analyse()
##dsv = param_filter_query(data_store,st_name='DriftingSinusoidalGratingDisk',analysis_algorithm=['TrialAveragedFiringRate'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'spatial_frequency', 'neurons': list(sorted(param_filter_query(data_store,sheet_name="X_ON").get_segments()[0].get_stored_esyn_ids())), 'sheet_name' : 'X_ON','centered' : False,'mean' : False})).plot({'TuningCurve F0_Inh_Cond.y_lim' : (0,180) , 'TuningCurve F0_Exc_Cond.y_lim' : (0,80)})
#import pylab
#pylab.show()
if True:
dsv = param_filter_query(data_store,
st_name='NaturalImageWithEyeMovement')
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': l4_exc,
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc2.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc3.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[1],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc4.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[2],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc5.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[3],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc6.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[4],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc7.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[5],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc8.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[6],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc9.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[7],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc10.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(
dsv,
ParameterSet({
'sheet_name':
'X_ON',
'neuron':
sorted(
param_filter_query(data_store,
sheet_name="X_ON").get_segments()
[0].get_stored_esyn_ids())[0],
'sheet_activity': {}
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="LGN0On.png").plot()
import pylab
pylab.show()
if False:
dsv = param_filter_query(
data_store, st_name='DriftingSineGratingWithEyeMovement')
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': l4_exc,
'sheet_activity': {}
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc2.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc3.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[1],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc4.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[2],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc5.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[3],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc6.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[4],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc7.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[5],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc8.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[6],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc9.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[7],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc10.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
# tuninc curves
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
analysis_algorithm=['TrialAveragedFiringRate', 'Analog_F0andF1'])
PlotTuningCurve(dsv,
ParameterSet({
'parameter_name': 'orientation',
'neurons': list(analog_ids),
'sheet_name': 'V1_Exc_L4',
'centered': True,
'mean': False,
'polar': False,
'pool': False
}),
fig_param={
'dpi': 100,
'figsize': (25, 12)
},
plot_file_name="TCExc.png").plot({
'TuningCurve F0_Inh_Cond.y_lim': (0, 180),
'TuningCurve F0_Exc_Cond.y_lim': (0, 80)
})
PlotTuningCurve(dsv,
ParameterSet({
'parameter_name': 'orientation',
'neurons': list(analog_ids_inh),
'sheet_name': 'V1_Inh_L4',
'centered': True,
'mean': False,
'polar': False,
'pool': False
}),
fig_param={
'dpi': 100,
'figsize': (25, 12)
},
plot_file_name="TCInh.png").plot({
'TuningCurve F0_Inh_Cond.y_lim': (0, 180),
'TuningCurve F0_Exc_Cond.y_lim': (0, 80)
})
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
analysis_algorithm=['Analog_MeanSTDAndFanoFactor'])
PlotTuningCurve(dsv,
ParameterSet({
'parameter_name': 'orientation',
'neurons': list(analog_ids),
'sheet_name': 'V1_Exc_L4',
'centered': True,
'mean': False,
'polar': False,
'pool': False
}),
fig_param={
'dpi': 100,
'figsize': (25, 12)
},
plot_file_name="TCExcA.png").plot()
PlotTuningCurve(dsv,
ParameterSet({
'parameter_name': 'orientation',
'neurons': list(analog_ids_inh),
'sheet_name': 'V1_Inh_L4',
'centered': True,
'mean': False,
'polar': False,
'pool': False
}),
fig_param={
'dpi': 100,
'figsize': (25, 12)
},
plot_file_name="TCInhA.png").plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialVariability'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(analog_ids), 'sheet_name' : 'V1_Exc_L4'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialVariability'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(analog_ids_inh), 'sheet_name' : 'V1_Inh_L4'})).plot()
import pylab
pylab.show()
def perform_analysis_and_visualization(data_store):
analog_ids = param_filter_query(
data_store,
sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
analog_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
spike_ids = param_filter_query(
data_store,
sheet_name="V1_Exc_L4").get_segments()[0].get_stored_spike_train_ids()
spike_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L4").get_segments()[0].get_stored_spike_train_ids()
NeuronAnnotationsToPerNeuronValues(data_store, ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Exc_L4')
l4_exc_phase = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentPhase',
sheet_name='V1_Exc_L4')
l4_exc = analog_ids[numpy.argmin([
circular_dist(o, numpy.pi / 2, numpy.pi)
for (o, p) in zip(l4_exc_or[0].get_value_by_id(analog_ids),
l4_exc_phase[0].get_value_by_id(analog_ids))
])]
l4_inh_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Inh_L4')
l4_inh_phase = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentPhase',
sheet_name='V1_Inh_L4')
l4_inh = analog_ids_inh[numpy.argmin([
circular_dist(o, numpy.pi / 2, numpy.pi)
for (o, p) in zip(l4_inh_or[0].get_value_by_id(analog_ids_inh),
l4_inh_phase[0].get_value_by_id(analog_ids_inh))
])]
l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(
numpy.array([
circular_dist(o, numpy.pi / 2, numpy.pi)
for (o, p) in zip(l4_exc_or[0].values, l4_exc_phase[0].values)
]) < 0.1)[0]]
if False: #ANALYSIS
TrialAveragedFiringRate(
param_filter_query(data_store,
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'],
st_name='FullfieldDriftingSinusoidalGrating'),
ParameterSet({})).analyse()
TrialAveragedFiringRate(
param_filter_query(data_store,
st_direct_stimulation_name="None",
st_name='InternalStimulus'),
ParameterSet({})).analyse()
#Irregularity(param_filter_query(data_store,st_direct_stimulation_name="None",st_name='InternalStimulus'),ParameterSet({})).analyse()
PopulationMean(
param_filter_query(data_store,
st_direct_stimulation_name="None",
st_name='InternalStimulus'),
ParameterSet({})).analyse()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
analysis_algorithm='TrialAveragedFiringRate',
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'])
GaussianTuningCurveFit(dsv,
ParameterSet({'parameter_name':
'orientation'})).analyse()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'])
Analog_F0andF1(dsv, ParameterSet({})).analyse()
GSTA(param_filter_query(data_store, sheet_name='V1_Exc_L4'),
ParameterSet({
'neurons': list(analog_ids),
'length': 250.0
}),
tags=['GSTA']).analyse()
#Precision(param_filter_query(data_store,sheet_name='V1_Exc_L4'),ParameterSet({'neurons' : [l4_exc], 'bin_length' : 10.0 })).analyse()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
analysis_algorithm='TrialAveragedFiringRate',
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'])
PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage(
dsv, ParameterSet({'parameter_name': 'orientation'})).analyse()
dsv = param_filter_query(data_store, sheet_name='V1_Exc_L4')
ActionPotentialRemoval(dsv, ParameterSet({'window_length':
5.0})).analyse()
dsv = param_filter_query(data_store,
st_name='NaturalImageWithEyeMovement',
sheet_name='V1_Exc_L4',
analysis_algorithm='ActionPotentialRemoval')
TrialVariability(
dsv,
ParameterSet({
'vm': True,
'cond_exc': False,
'cond_inh': False
})).analyse()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name='V1_Exc_L4',
st_contrast=100,
analysis_algorithm='ActionPotentialRemoval')
TrialVariability(
dsv,
ParameterSet({
'vm': True,
'cond_exc': False,
'cond_inh': False
})).analyse()
dsv = param_filter_query(data_store,
st_name='InternalStimulus',
sheet_name='V1_Exc_L4')
Analog_MeanSTDAndFanoFactor(dsv, ParameterSet({})).analyse()
pnv = param_filter_query(
data_store,
st_name='InternalStimulus',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_MeanSTDAndFanoFactor'],
value_name='Mean(ECond)',
st_direct_stimulation_name='None').get_analysis_result()[0]
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_F0andF1'],
value_name='F0_Exc_Cond')
SubtractPNVfromPNVS(pnv, dsv, ParameterSet({})).analyse()
pnv = param_filter_query(
data_store,
st_name='InternalStimulus',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_MeanSTDAndFanoFactor'],
value_name='Mean(ICond)',
st_direct_stimulation_name='None').get_analysis_result()[0]
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_F0andF1'],
value_name='F0_Inh_Cond')
SubtractPNVfromPNVS(pnv, dsv, ParameterSet({})).analyse()
pnv = param_filter_query(
data_store,
st_name='InternalStimulus',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_MeanSTDAndFanoFactor'],
value_name='Mean(VM)',
st_direct_stimulation_name='None').get_analysis_result()[0]
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_F0andF1'],
value_name='F0_Vm')
SubtractPNVfromPNVS(pnv, dsv, ParameterSet({})).analyse()
data_store.save()
if True: # PLOTTING
activity_plot_param = {
'frame_rate': 5,
'bin_width': 5.0,
'scatter': True,
'resolution': 0
}
Kremkow_plots.ConductanceAndVmTuningSummary(
data_store,
ParameterSet({'many': False}),
fig_param={
'dpi': 100,
'figsize': (25, 16)
},
plot_file_name='Cond&VMTuning.png').plot()
# self sustained plotting
dsv = param_filter_query(data_store, st_name=['InternalStimulus'])
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[0],
'sheet_activity': {}
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSExcAnalog.png').plot()
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[1], 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name='SSExcAnalog1.png').plot()
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[2], 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name='SSExcAnalog2.png').plot()
RasterPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neurons': spike_ids,
'trial_averaged_histogram': False
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSExcRaster.png').plot(
{'SpikeRasterPlot.group_trials': True})
RasterPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Inh_L4',
'neurons': spike_ids_inh,
'trial_averaged_histogram': False
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSInhRaster.png').plot(
{'SpikeRasterPlot.group_trials': True})
orr = list(
set([
MozaikParametrized.idd(s).orientation
for s in queries.param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_contrast=100).get_stimuli()
]))
SNRAnalysis(data_store,
ParameterSet({"neuron": analog_ids[0]}),
fig_param={
'dpi': 100,
'figsize': (25, 12)
},
plot_file_name='SNR1.png').plot()
SNRAnalysis(data_store,
ParameterSet({"neuron": analog_ids[1]}),
fig_param={
'dpi': 100,
'figsize': (25, 12)
},
plot_file_name='SNR2.png').plot()
SNRAnalysis(data_store,
ParameterSet({"neuron": analog_ids[2]}),
fig_param={
'dpi': 100,
'figsize': (25, 12)
},
plot_file_name='SNR3.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[0],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison1.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[1],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison2.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[2],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison3.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[3],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison4.png').plot()
TrialToTrialVariabilityComparison(data_store,
ParameterSet({}),
plot_file_name='TtTVar.png').plot()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=numpy.pi / 2,
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': l4_exc,
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview.png').plot()
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=orr[numpy.argmin([
circular_dist(o, l4_exc_or[0].get_value_by_id(analog_ids[0]),
numpy.pi) for o in orr
])],
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[0],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview1.png').plot()
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=orr[numpy.argmin([
circular_dist(o, l4_exc_or[0].get_value_by_id(analog_ids[1]),
numpy.pi) for o in orr
])],
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[1],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview2.png').plot()
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=orr[numpy.argmin([
circular_dist(o, l4_exc_or[0].get_value_by_id(analog_ids[2]),
numpy.pi) for o in orr
])],
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[2],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview3.png').plot()
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=orr[numpy.argmin([
circular_dist(o, l4_exc_or[0].get_value_by_id(analog_ids[3]),
numpy.pi) for o in orr
])],
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[3],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview4.png').plot()
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=orr[numpy.argmin([
circular_dist(o, l4_exc_or[0].get_value_by_id(analog_ids[4]),
numpy.pi) for o in orr
])],
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[4],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview5.png').plot()
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=orr[numpy.argmin([
circular_dist(o, l4_exc_or[0].get_value_by_id(analog_ids[5]),
numpy.pi) for o in orr
])],
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[5],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview6.png').plot()
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=orr[numpy.argmin([
circular_dist(o, l4_exc_or[0].get_value_by_id(analog_ids[5]),
numpy.pi) for o in orr
])],
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[6],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview7.png').plot()
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=orr[numpy.argmin([
circular_dist(o, l4_exc_or[0].get_value_by_id(analog_ids[6]),
numpy.pi) for o in orr
])],
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[7],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview8.png').plot()
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=orr[numpy.argmin([
circular_dist(o, l4_exc_or[0].get_value_by_id(analog_ids[7]),
numpy.pi) for o in orr
])],
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[8],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview9.png').plot()
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=orr[numpy.argmin([
circular_dist(o, l4_exc_or[0].get_value_by_id(analog_ids[8]),
numpy.pi) for o in orr
])],
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[9],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview10.png').plot()
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=orr[numpy.argmin([
circular_dist(o, l4_exc_or[0].get_value_by_id(analog_ids[9]),
numpy.pi) for o in orr
])],
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[10],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview11.png').plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',st_orientation=numpy.pi/2,st_contrast=100)
#KremkowOverviewFigure(dsv,ParameterSet({'neuron' : l4_exc,'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name='ExcOverview.png').plot()
dsv = param_filter_query(data_store,
st_name='NaturalImageWithEyeMovement')
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': l4_exc,
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='NMOverview.png').plot()
#Kremkow_plots.StimulusResponseComparison(data_store,ParameterSet({'neuron' : l4_exc,'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name='StimulusResponseComparison.png').plot()
Kremkow_plots.OrientationTuningSummary(
data_store,
ParameterSet({}),
fig_param={
'dpi': 100,
'figsize': (20, 12)
},
plot_file_name='OrientationTuningSummary.png').plot()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating')
RasterPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neurons': spike_ids,
'trial_averaged_histogram': False
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='EvokedExcRaster.png').plot(
{'SpikeRasterPlot.group_trials': True})
RasterPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Inh_L4',
'neurons': spike_ids_inh,
'trial_averaged_histogram': False
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='EvokedInhRaster.png').plot(
{'SpikeRasterPlot.group_trials': True})
#OverviewPlot(data_store,ParameterSet({'sheet_name' : 'X_ON', 'neuron' : sorted(param_filter_query(data_store,sheet_name="X_ON").get_segments()[0].get_stored_esyn_ids())[0], 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="LGN0On.png").plot()
#OverviewPlot(data_store,ParameterSet({'sheet_name' : 'X_OFF', 'neuron' : sorted(param_filter_query(data_store,sheet_name="X_OFF").get_segments()[0].get_stored_esyn_ids())[0], 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="LGN0Off.png").plot()
# orientation tuning plotting
dsv = param_filter_query(data_store,
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'],
value_name='LGNAfferentOrientation')
PerNeuronValuePlot(dsv,
ParameterSet({"cortical_view": True}),
plot_file_name='ORSet.png').plot()
dsv = param_filter_query(
data_store,
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'],
value_name='orientation preference',
analysis_algorithm=
'PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage',
st_contrast=100)
PerNeuronValuePlot(dsv,
ParameterSet({"cortical_view": True}),
plot_file_name='ORComputed.png').plot()
dsv = param_filter_query(data_store,
value_name=['orientation HWHH'],
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'])
PerNeuronValueScatterPlot(dsv,
ParameterSet({}),
plot_file_name='HWHH.png',
fig_param={
'dpi': 100,
'figsize': (28, 12)
}).plot({
'ScatterPlot.x_lim': (0, 90),
'ScatterPlot.y_lim': (0, 90),
'ScatterPlot.identity_line': True
})
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating')
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': l4_exc,
'sheet_activity': {}
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="Exc.png").plot(
{'Vm_plot.y_lim': (-90, -50)})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Inh_L4',
'neuron': l4_inh,
'sheet_activity': {}
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="Inh.png").plot(
{'Vm_plot.y_lim': (-90, -50)})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[0],
'sheet_activity': {}
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="Exc1.png").plot(
{'Vm_plot.y_lim': (-90, -50)})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[1],
'sheet_activity': {}
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="Exc2.png").plot(
{'Vm_plot.y_lim': (-90, -50)})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[2],
'sheet_activity': {}
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="Exc3.png").plot(
{'Vm_plot.y_lim': (-90, -50)})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[3],
'sheet_activity': {}
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="Exc4.png").plot(
{'Vm_plot.y_lim': (-90, -50)})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[4],
'sheet_activity': {}
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="Exc5.png").plot(
{'Vm_plot.y_lim': (-90, -50)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'X_ON', 'neuron' : sorted(param_filter_query(data_store,sheet_name="X_ON").get_segments()[0].get_stored_esyn_ids())[0], 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="LGN0On.png").plot()
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'X_OFF', 'neuron' : sorted(param_filter_query(data_store,sheet_name="X_OFF").get_segments()[0].get_stored_esyn_ids())[0], 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="LGN0Off.png").plot()
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'X_ON', 'neuron' : sorted(param_filter_query(data_store,sheet_name="X_ON").get_segments()[0].get_stored_esyn_ids())[1], 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="LGN1On.png").plot()
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'X_OFF', 'neuron' : sorted(param_filter_query(data_store,sheet_name="X_OFF").get_segments()[0].get_stored_esyn_ids())[1], 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="LGN1Off.png").plot()
# tuninc curves
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
analysis_algorithm=['TrialAveragedFiringRate', 'Analog_F0andF1'])
PlotTuningCurve(dsv,
ParameterSet({
'parameter_name': 'orientation',
'neurons': list(analog_ids),
'sheet_name': 'V1_Exc_L4',
'centered': True,
'mean': False,
'polar': False,
'pool': False
}),
plot_file_name='TCsExc.png',
fig_param={
'dpi': 100,
'figsize': (28, 12)
}).plot({
'TuningCurve F0_Inh_Cond.y_lim': (0, 180),
'TuningCurve F0_Exc_Cond.y_lim': (0, 80)
})
PlotTuningCurve(dsv,
ParameterSet({
'parameter_name': 'orientation',
'neurons': list(analog_ids_inh),
'sheet_name': 'V1_Inh_L4',
'centered': True,
'mean': False,
'polar': False,
'pool': False
}),
plot_file_name='TCsInh.png',
fig_param={
'dpi': 100,
'figsize': (28, 12)
}).plot({
'TuningCurve F0_Inh_Cond.y_lim': (0, 180),
'TuningCurve F0_Exc_Cond.y_lim': (0, 80)
})
dsv = param_filter_query(data_store,
st_name='NaturalImageWithEyeMovement')
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': l4_exc,
'sheet_activity': {}
}),
plot_file_name='NMExc.png',
fig_param={
'dpi': 100,
'figsize': (28, 12)
}).plot({
'Vm_plot.y_lim': (-70, -50),
'Conductance_plot.y_lim': (0, 50.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Inh_L4',
'neuron': l4_inh,
'sheet_activity': {}
}),
plot_file_name='NMInh.png',
fig_param={
'dpi': 100,
'figsize': (28, 12)
}).plot({
'Vm_plot.y_lim': (-70, -50),
'Conductance_plot.y_lim': (0, 50.0)
})
pylab.show()
def perform_analysis_and_visualization(data_store):
pref_or = numpy.pi / 2
#find neuron with preference closet to pref_or
l4_analog_ids = param_filter_query(
data_store,
sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
l4_analog_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
l23_analog_ids = param_filter_query(
data_store,
sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_esyn_ids()
l23_analog_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L2/3").get_segments()[0].get_stored_esyn_ids()
NeuronAnnotationsToPerNeuronValues(data_store, ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Exc_L4')
l4_exc = l4_analog_ids[numpy.argmin([
circular_dist(o, pref_or, numpy.pi)
for o in l4_exc_or[0].get_value_by_id(l4_analog_ids)
])]
l4_inh_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Inh_L4')
l4_inh = l4_analog_ids_inh[numpy.argmin([
circular_dist(o, pref_or, numpy.pi)
for o in l4_inh_or[0].get_value_by_id(l4_analog_ids_inh)
])]
l23_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Exc_L2/3')
l23_exc = l23_analog_ids[numpy.argmin([
circular_dist(o, pref_or, numpy.pi)
for o in l23_exc_or[0].get_value_by_id(l23_analog_ids)
])]
l23_inh_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Inh_L2/3')
l23_inh = l23_analog_ids_inh[numpy.argmin([
circular_dist(o, pref_or, numpy.pi)
for o in l23_inh_or[0].get_value_by_id(l23_analog_ids_inh)
])]
#l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(numpy.array([circular_dist(o,pref_or,numpy.pi) for (o,p) in zip(l4_exc_or[0].values,l4_exc_phase[0].values)]) < 0.1)[0]]
# Find neurons for which spikes were recorded and whose orientation is close to pref_or
l4_spike_ids = param_filter_query(
data_store,
sheet_name="V1_Exc_L4").get_segments()[0].get_stored_spike_train_ids()
l4_spike_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L4").get_segments()[0].get_stored_spike_train_ids()
l23_spike_ids = param_filter_query(data_store,
sheet_name="V1_Exc_L2/3").get_segments(
)[0].get_stored_spike_train_ids()
l23_spike_ids_inh = param_filter_query(
data_store, sheet_name="V1_Inh_L2/3").get_segments(
)[0].get_stored_spike_train_ids()
l4_spike_exc_or_close_to_pi_half = numpy.array(l4_spike_ids)[numpy.nonzero(
numpy.array([
circular_dist(o, pref_or, numpy.pi)
for o in l4_exc_or[0].get_value_by_id(l4_spike_ids)
]) < 0.1)[0]].tolist()
l4_spike_inh_or_close_to_pi_half = numpy.array(l4_spike_ids_inh)[
numpy.nonzero(
numpy.array([
circular_dist(o, pref_or, numpy.pi)
for o in l4_inh_or[0].get_value_by_id(l4_spike_ids_inh)
]) < 0.1)[0]].tolist()
l23_spike_exc_or_close_to_pi_half = numpy.array(l23_spike_ids)[
numpy.nonzero(
numpy.array([
circular_dist(o, pref_or, numpy.pi)
for o in l23_exc_or[0].get_value_by_id(l23_spike_ids)
]) < 0.1)[0]].tolist()
l23_spike_inh_or_close_to_pi_half = numpy.array(l23_spike_ids_inh)[
numpy.nonzero(
numpy.array([
circular_dist(o, pref_or, numpy.pi)
for o in l23_inh_or[0].get_value_by_id(l23_spike_ids_inh)
]) < 0.1)[0]].tolist()
print "Prefered orientation of plotted exc neurons:"
print 'id: ' + str(l4_exc)
print "Prefered orientation of plotted inh neurons:"
print 'id: ' + str(l4_inh)
if True: #ANALYSIS
#TrialAveragedFiringRate(data_store,ParameterSet({'stimulus_type':"DriftingSinusoidalGratingCenterSurroundStimulus"})).analyse()
TrialAveragedFiringRate(data_store, ParameterSet({})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm='TrialAveragedFiringRate')
#GaussianTuningCurveFit(dsv,ParameterSet({'parameter_name' : 'orientation'})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'])
#Analog_F0andF1(dsv,ParameterSet({})).analyse()
#TrialVariability(dsv,ParameterSet({'vm': True, 'cond_exc': False, 'cond_inh': False})).analyse()
#TrialMean(dsv,ParameterSet({'vm': True, 'cond_exc': False, 'cond_inh': False})).analyse()
#GSTA(param_filter_query(data_store,sheet_name='V1_Exc_L4'),ParameterSet({'neurons' : [l4_exc], 'length' : 250.0 }),tags=['GSTA']).analyse()
#Precision(param_filter_query(data_store,sheet_name='V1_Exc_L4'),ParameterSet({'neurons' : [l4_exc], 'bin_length' : 10.0 })).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'])
#PSTH(dsv,ParameterSet({'bin_length' : 10.0})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'],y_axis_name='psth (bin=10.0)',st_contrast=100,analysis_algorithm='PSTH')
#dsv1 = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'],value_name='orientation preference',st_contrast=100,analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage')
#ModulationRatio(dsv+dsv1,ParameterSet({})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm='TrialAveragedFiringRate',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'])
#PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage(dsv,ParameterSet({'parameter_name' : 'orientation'})).analyse()
#PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage(dsv,ParameterSet({'parameter_name' : 'phase'})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4'],value_name='orientation preference',st_contrast=100,analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage')
#LocalHomogeneityIndex(dsv,ParameterSet({'sigma' : 0.1})).analyse()
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4'],value_name='LGNAfferentOrientation')
#LocalHomogeneityIndex(dsv,ParameterSet({'sigma' : 100.0})).analyse()
data_store.save()
if True: # PLOTTING
#verify_connectivity(data_store)
activity_plot_param = {
'frame_rate': 5,
'bin_width': 20.0,
'scatter': True,
'resolution': 0
}
#dsv = param_filter_query(data_store,st_name='NaturalImageWithEyeMovement')
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#dsv = param_filter_query(data_store,st_name='DriftingGratingWithEyeMovement')
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4'],value_name='LocalHomogeneityIndex' + '(0.1:orientation preference)')
#PerNeuronValuePlot(dsv,ParameterSet({})).plot({'*.colorbar' : False,'*.x_axis' : None, '*.y_axis' : None})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4'],value_name='LocalHomogeneityIndex' + '(100.0:LGNAfferentOrientation)')
#PerNeuronValuePlot(dsv,ParameterSet({})).plot({'*.colorbar' : False,'*.x_axis' : None, '*.y_axis' : None})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'],value_name='LGNAfferentOrientation')
#PerNeuronValuePlot(dsv,ParameterSet({})).plot({'*.colorbar' : False,'*.x_axis' : None, '*.y_axis' : None})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'],value_name='orientation preference',analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage',st_contrast=100)
#PerNeuronValuePlot(dsv,ParameterSet({})).plot({'*.colorbar' : False, '*.x_axis' : None, '*.y_axis' : None})
#TuningComparison(data_store,ParameterSet({}),fig_param={'dpi' : 100,'figsize': (17,7.5)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/TuningComparison.png").plot({'*.colorbar' : False,'*.x_axis' : None, '*.y_axis' : None})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4'],analysis_algorithm='TrialVariability',st_orientation = 0)
#PerNeuronValueScatterPlot(dsv,ParameterSet({})).plot({'ScatterPlot.identity_line' : True, 'ScatterPlot.mark_means' : True})
#dsv = param_filter_query(data_store,value_name=['orientation HWHH'],sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'])
#PerNeuronValueScatterPlot(dsv,ParameterSet({}),plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/MR.png").plot({ 'ScatterPlot.x_lim' : (0,90), 'ScatterPlot.y_lim' : (0,90), 'ScatterPlot.identity_line' : True})
#HWHH(data_store,ParameterSet({}),fig_param={'dpi' : 100,'figsize': (8,12)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/HWHH.png").plot({ '*.x_lim' : (0,90), '*.y_lim' : (0,90), '*.identity_line' : True, '*.title' : None})
# LHI orientation selectivity correlation
#dsv1 = param_filter_query(data_store,value_name='orientation selectivity',sheet_name=['V1_Exc_L4'],analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage',st_contrast=100)
#dsv2 = param_filter_query(data_store,value_name='LocalHomogeneityIndex' + '(0.1:orientation preference)',sheet_name=['V1_Exc_L4'])
#PerNeuronValueScatterPlot(dsv1+dsv2,ParameterSet({}),plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/LHIvsSelectivity.png").plot()
#dsv = param_filter_query(data_store,st_orientation=[0,numpy.pi/2],st_name='DriftingSinusoidalGratingDisk',st_contrast=100)
#dsv = param_filter_query(data_store,st_orientation=[0,numpy.pi/2],st_name=['FullfieldDriftingSinusoidalGrating'],st_contrast=100)
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="OverviewEXCL4.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="OverviewEXCL23.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="OverviewINHL4.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="OverviewINHL23.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#dsv = param_filter_query(data_store,st_name='Null')
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
# PLOT ORIENTATION TUNING
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
analysis_algorithm=['TrialAveragedFiringRate'])
PlotTuningCurve(dsv,
ParameterSet({
'parameter_name': 'contrast',
'neurons': l4_spike_exc_or_close_to_pi_half,
'sheet_name': 'V1_Exc_L4'
}),
plot_file_name="CTC.png",
fig_param={
'dpi': 200,
'figsize': (14, 3)
}).plot({'TuningCurve_firing_rate.y_lim': (0, 50)})
CTC(dsv,
ParameterSet({
'l4_exc_neurons':
numpy.array(l4_spike_exc_or_close_to_pi_half)[:4].tolist(),
'l4_inh_neurons':
numpy.array(l4_spike_inh_or_close_to_pi_half)[:4].tolist(),
'l23_exc_neurons':
numpy.array(l23_spike_exc_or_close_to_pi_half)[:4].tolist(),
'l23_inh_neurons':
numpy.array(l23_spike_inh_or_close_to_pi_half)[:4].tolist()
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="CTC.svg").plot({'*.title': None})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'contrast', 'neurons': l4_spike_inh_or_close_to_pi_half, 'sheet_name' : 'V1_Inh_L4'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'contrast', 'neurons': l23_spike_exc_or_close_to_pi_half, 'sheet_name' : 'V1_Exc_L2/3'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'contrast', 'neurons': l23_spike_inh_or_close_to_pi_half, 'sheet_name' : 'V1_Inh_L2/3'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
# PLOT SIZETUNING
#dsv = param_filter_query(data_store,st_name='DriftingSinusoidalGratingDisk',analysis_algorithm=['TrialAveragedFiringRate'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'radius', 'neurons': l4_spike_exc_or_close_to_pi_half, 'sheet_name' : 'V1_Exc_L4'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'radius', 'neurons': l4_spike_inh_or_close_to_pi_half, 'sheet_name' : 'V1_Inh_L4'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'radius', 'neurons': l23_spike_exc_or_close_to_pi_half, 'sheet_name' : 'V1_Exc_L2/3'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'radius', 'neurons': l23_spike_inh_or_close_to_pi_half, 'sheet_name' : 'V1_Inh_L2/3'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#STC(dsv,ParameterSet({'l4_exc_neurons' : numpy.array(l4_spike_exc_or_close_to_pi_half)[:6].tolist(),'l4_inh_neurons' : numpy.array(l4_spike_inh_or_close_to_pi_half)[:6].tolist(),'l23_exc_neurons' : numpy.array(l23_spike_exc_or_close_to_pi_half)[:6].tolist(),'l23_inh_neurons' : numpy.array(l23_spike_inh_or_close_to_pi_half)[:6].tolist()}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/STC.png").plot({'*.title' : None})
# PLOT OCTC
#dsv = param_filter_query(data_store,st_name='DriftingSinusoidalGratingCenterSurroundStimulus',analysis_algorithm=['TrialAveragedFiringRate'])
#OCTC(dsv,ParameterSet({'l4_exc_neurons' : numpy.array(l4_spike_exc_or_close_to_pi_half)[:5].tolist(),'l4_inh_neurons' : numpy.array(l4_spike_inh_or_close_to_pi_half)[:5].tolist(),'l23_exc_neurons' : numpy.array(l23_spike_exc_or_close_to_pi_half).tolist()[:5],'l23_inh_neurons' : numpy.array(l23_spike_inh_or_close_to_pi_half)[:5].tolist()}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/OCTC.png").plot({'*.title' : None})
# tuninc curves
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate','Analog_F0andF1'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l4_analog_ids), 'sheet_name' : 'V1_Exc_L4'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate','Analog_F0andF1'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l23_analog_ids), 'sheet_name' : 'V1_Exc_L2/3'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate','Analog_F0andF1'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l4_analog_ids_inh), 'sheet_name' : 'V1_Inh_L4'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate','Analog_F0andF1'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l23_analog_ids_inh), 'sheet_name' : 'V1_Inh_L2/3'})).plot()
#OR(dsv,ParameterSet({'l4_exc_neurons' : numpy.array(l4_spike_exc_or_close_to_pi_half)[:4].tolist(),'l4_inh_neurons' : numpy.array(l4_spike_inh_or_close_to_pi_half)[:4].tolist(),'l23_exc_neurons' : numpy.array(l23_spike_exc_or_close_to_pi_half)[:4].tolist(),'l23_inh_neurons' : numpy.array(l23_spike_inh_or_close_to_pi_half)[:4].tolist()}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/OR.png").plot({'*.title' : None})
#MRfig(data_store,ParameterSet({'SimpleSheetName' : 'V1_Exc_L4', 'ComplexSheetName' : 'V1_Exc_L2/3',}),fig_param={'dpi' : 100,'figsize': (8,6)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/MR.png").plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialVariability'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l4_analog_ids), 'sheet_name' : 'V1_Exc_L4'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialVariability'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l4_analog_ids_inh), 'sheet_name' : 'V1_Inh_L4'})).plot()
import pylab
pylab.show()
def perform_analysis_and_visualization(data_store):
analog_ids = param_filter_query(data_store,sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
analog_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
spike_ids = param_filter_query(data_store,sheet_name="V1_Exc_L4").get_segments()[0].get_stored_spike_train_ids()
spike_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L4").get_segments()[0].get_stored_spike_train_ids()
spike_ids_on = param_filter_query(data_store,sheet_name="X_ON").get_segments()[0].get_stored_spike_train_ids()
spike_ids_off = param_filter_query(data_store,sheet_name="X_OFF").get_segments()[0].get_stored_spike_train_ids()
if True: # PLOTTING
activity_plot_param = {
'frame_rate' : 5,
'bin_width' : 5.0,
'scatter' : True,
'resolution' : 0
}
#PSTH(param_filter_query(data_store,st_direct_stimulation_name="None",st_name=['Null']),ParameterSet({'bin_length' : 5.0})).analyse()
#TrialAveragedFiringRate(param_filter_query(data_store,st_direct_stimulation_name="None",st_name=['Null']),ParameterSet({})).analyse()
#Irregularity(param_filter_query(data_store,st_direct_stimulation_name="None",st_name=['Null']),ParameterSet({})).analyse()
#NeuronToNeuronAnalogSignalCorrelations(param_filter_query(data_store,analysis_algorithm='PSTH'),ParameterSet({'convert_nan_to_zero' : True})).analyse()
#PopulationMean(data_store,ParameterSet({})).analyse()
#data_store.print_content(full_ADS=True)
#find neuron with preference closet to 0
NeuronAnnotationsToPerNeuronValues(data_store,ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Exc_L4')
l4_exc_phase = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentPhase', sheet_name = 'V1_Exc_L4')
l4_exc = analog_ids[numpy.argmin([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_exc_or[0].get_value_by_id(analog_ids),l4_exc_phase[0].get_value_by_id(analog_ids))])]
l4_inh_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Inh_L4')
l4_inh_phase = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentPhase', sheet_name = 'V1_Inh_L4')
l4_inh = analog_ids_inh[numpy.argmin([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_inh_or[0].get_value_by_id(analog_ids_inh),l4_inh_phase[0].get_value_by_id(analog_ids_inh))])]
l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(numpy.array([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_exc_or[0].values,l4_exc_phase[0].values)]) < 0.1)[0]]
print "Prefered orientation of plotted exc neurons:"
print 'index ' + str(l4_exc)
print "Prefered phase of plotted exc neurons:"
print l4_exc_phase[0].get_value_by_id(l4_exc)
print "Prefered orientation of plotted inh neurons:"
print l4_inh_phase[0].get_value_by_id(l4_inh)
print 'index ' + str(l4_inh)
print "Prefered phase of plotted inh neurons:"
print l4_exc_phase[0].get_value_by_id(l4_exc)
OverviewPlot(data_store,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name='ExcAnalog.png').plot({'Vm_plot.y_lim' : (-80,-50),'Conductance_plot.y_lim' : (0,100.0)})
OverviewPlot(data_store,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[1], 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (19,12)}).plot({'Vm_plot.y_lim' : (-80,-50),'Conductance_plot.y_lim' : (0,100.0)})
OverviewPlot(data_store,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[2], 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (19,12)}).plot({'Vm_plot.y_lim' : (-80,-50),'Conductance_plot.y_lim' : (0,100.0)})
OverviewPlot(data_store,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name='InhAnalog.png').plot({'Vm_plot.y_lim' : (-80,-50),'Conductance_plot.y_lim' : (0,100.0)})
OverviewPlot(data_store,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : analog_ids_inh[1], 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (19,12)}).plot({'Vm_plot.y_lim' : (-80,-50),'Conductance_plot.y_lim' : (0,100.0)})
OverviewPlot(data_store,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : analog_ids_inh[2], 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (19,12)}).plot({'Vm_plot.y_lim' : (-80,-50),'Conductance_plot.y_lim' : (0,100.0)})
RasterPlot(param_filter_query(data_store,st_name=['Null']),ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neurons' : spike_ids,'trial_averaged_histogram': False}),fig_param={'dpi' : 100,'figsize': (17,5)},plot_file_name='ExcRaster.png').plot({'SpikeRasterPlot.group_trials':True})
RasterPlot(param_filter_query(data_store,st_name=['Null']),ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neurons' : spike_ids_inh,'trial_averaged_histogram': False}),fig_param={'dpi' : 100,'figsize': (17,5)},plot_file_name='InhRaster.png').plot({'SpikeRasterPlot.group_trials':True})
RasterPlot(param_filter_query(data_store,st_name=['Null']),ParameterSet({'sheet_name' : 'X_ON', 'neurons' : spike_ids_on,'trial_averaged_histogram': False}),fig_param={'dpi' : 100,'figsize': (17,5)},plot_file_name='ExcRaster.png').plot({'SpikeRasterPlot.group_trials':True})
RasterPlot(param_filter_query(data_store,st_name=['Null']),ParameterSet({'sheet_name' : 'X_OFF', 'neurons' : spike_ids_off,'trial_averaged_histogram': False}),fig_param={'dpi' : 100,'figsize': (17,5)},plot_file_name='InhRaster.png').plot({'SpikeRasterPlot.group_trials':True})
import pylab
pylab.show()
from mozaik.storage import queries
from mozaik.controller import Global
Global.root_directory = sys.argv[1] + '/'
setup_logging()
data_store = PickledDataStore(load=True,
parameters=ParameterSet({
'root_directory':
sys.argv[1],
'store_stimuli':
False
}),
replace=True)
NeuronAnnotationsToPerNeuronValues(data_store, ParameterSet({})).analyse()
analog_ids = queries.param_filter_query(
data_store,
sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
dsv = queries.param_filter_query(data_store, st_name='FlashedBar')
for ads in dsv.get_analysis_result():
sid = MozaikParametrized.idd(ads.stimulus_id)
sid.x = 0
ads.stimulus_id = str(sid)
for seg in dsv.get_segments():
sid = MozaikParametrized.idd(seg.annotations['stimulus'])
sid.x = 0
seg.annotations['stimulus'] = str(sid)
for seg in dsv.get_segments(null=True):
sid = MozaikParametrized.idd(seg.annotations['stimulus'])
def perform_analysis_and_visualization(data_store):
sheets = list(set(data_store.sheets()) & set(
['V1_Exc_L4', 'V1_Inh_L4', 'V1_Exc_L2/3', 'V1_Inh_L2/3']))
exc_sheets = list(set(data_store.sheets()) &
set(['V1_Exc_L4', 'V1_Exc_L2/3']))
l23_flag = 'V1_Exc_L2/3' in set(sheets)
NeuronAnnotationsToPerNeuronValues(data_store, ParameterSet({})).analyse()
analog_ids = param_filter_query(data_store, sheet_name="V1_Exc_L4").get_segments()[
0].get_stored_esyn_ids()
analog_ids_inh = param_filter_query(
data_store, sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
spike_ids = param_filter_query(data_store, sheet_name="V1_Exc_L4").get_segments()[
0].get_stored_spike_train_ids()
spike_ids_inh = param_filter_query(data_store, sheet_name="V1_Inh_L4").get_segments()[
0].get_stored_spike_train_ids()
if l23_flag:
analog_ids23 = param_filter_query(
data_store, sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_esyn_ids()
analog_ids_inh23 = param_filter_query(
data_store, sheet_name="V1_Inh_L2/3").get_segments()[0].get_stored_esyn_ids()
spike_ids23 = param_filter_query(
data_store, sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_spike_train_ids()
spike_ids_inh23 = param_filter_query(
data_store, sheet_name="V1_Inh_L2/3").get_segments()[0].get_stored_spike_train_ids()
else:
analog_ids23 = None
analog_ids_inh23 = None
if l23_flag:
l23_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentOrientation', sheet_name='V1_Exc_L2/3')[0]
l23_inh_or = data_store.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentOrientation', sheet_name='V1_Inh_L2/3')[0]
l4_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentOrientation', sheet_name='V1_Exc_L4')
l4_exc_phase = data_store.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentPhase', sheet_name='V1_Exc_L4')
l4_exc = analog_ids[numpy.argmin([circular_dist(o, 0, numpy.pi) for (o, p) in zip(
l4_exc_or[0].get_value_by_id(analog_ids), l4_exc_phase[0].get_value_by_id(analog_ids))])]
l4_inh_or = data_store.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentOrientation', sheet_name='V1_Inh_L4')
l4_inh_phase = data_store.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentPhase', sheet_name='V1_Inh_L4')
l4_inh = analog_ids_inh[numpy.argmin([circular_dist(o, 0, numpy.pi) for (o, p) in zip(
l4_inh_or[0].get_value_by_id(analog_ids_inh), l4_inh_phase[0].get_value_by_id(analog_ids_inh))])]
l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(numpy.array([circular_dist(
o, 0, numpy.pi) for (o, p) in zip(l4_exc_or[0].values, l4_exc_phase[0].values)]) < 0.1)[0]]
l4_exc_or_many = list(set(l4_exc_or_many) & set(spike_ids))
if l23_flag:
l23_exc_or_many = numpy.array(l23_exc_or.ids)[numpy.nonzero(numpy.array(
[circular_dist(o, 0, numpy.pi) for o in l23_exc_or.values]) < 0.1)[0]]
l23_exc_or_many = list(set(l23_exc_or_many) & set(spike_ids23))
orr = list(set([MozaikParametrized.idd(s).orientation for s in queries.param_filter_query(
data_store, st_name='FullfieldDriftingSinusoidalGrating', st_contrast=100).get_stimuli()]))
l4_exc_or_many_analog = numpy.array(analog_ids)[numpy.nonzero(numpy.array(
[circular_dist(l4_exc_or[0].get_value_by_id(i), 0, numpy.pi) for i in analog_ids]) < 0.1)[0]]
l4_inh_or_many_analog = numpy.array(analog_ids_inh)[numpy.nonzero(numpy.array(
[circular_dist(l4_inh_or[0].get_value_by_id(i), 0, numpy.pi) for i in analog_ids_inh]) < 0.15)[0]]
if l23_flag:
l23_inh_or_many_analog = numpy.array(analog_ids_inh23)[numpy.nonzero(numpy.array(
[circular_dist(l23_inh_or.get_value_by_id(i), 0, numpy.pi) for i in analog_ids_inh23]) < 0.15)[0]]
l23_exc_or_many_analog = numpy.array(analog_ids23)[numpy.nonzero(numpy.array(
[circular_dist(l23_exc_or.get_value_by_id(i), 0, numpy.pi) for i in analog_ids23]) < 0.1)[0]]
if True:
if l23_flag:
analysis(data_store, analog_ids, analog_ids_inh,
analog_ids23, analog_ids_inh23)
else:
analysis(data_store, analog_ids, analog_ids_inh)
if True: # PLOTTING
activity_plot_param = {
'frame_rate': 5,
'bin_width': 5.0,
'scatter': True,
'resolution': 0
}
# self sustained plotting
dsv = param_filter_query(data_store, st_name=[
'InternalStimulus'], st_direct_stimulation_name=None)
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neuron': analog_ids[0], 'sheet_activity': {
}, 'spontaneous': True}), fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='SSExcAnalog.png').plot()
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L4', 'neuron': analog_ids_inh[0], 'sheet_activity': {
}, 'spontaneous': True}), fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='SSInhAnalog.png').plot()
RasterPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neurons': spike_ids, 'trial_averaged_histogram': False, 'spontaneous': False}), fig_param={
'dpi': 100, 'figsize': (28, 12)}, plot_file_name='SSExcRasterL4.png').plot({'SpikeRasterPlot.group_trials': True})
RasterPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L4', 'neurons': spike_ids_inh, 'trial_averaged_histogram': False, 'spontaneous': False}), fig_param={
'dpi': 100, 'figsize': (28, 12)}, plot_file_name='SSInhRasterL4.png').plot({'SpikeRasterPlot.group_trials': True})
if l23_flag:
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L2/3', 'neuron': analog_ids23[0], 'sheet_activity': {
}, 'spontaneous': True}), fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='SSExcAnalog23.png').plot()
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L2/3', 'neuron': analog_ids_inh23[0], 'sheet_activity': {
}, 'spontaneous': True}), fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='SSInhAnalog23.png').plot()
RasterPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L2/3', 'neurons': spike_ids23, 'trial_averaged_histogram': False, 'spontaneous': False}),
fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='SSExcRasterL23.png').plot({'SpikeRasterPlot.group_trials': True})
RasterPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L2/3', 'neurons': spike_ids_inh23, 'trial_averaged_histogram': False, 'spontaneous': False}),
fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='SSInhRasterL23.png').plot({'SpikeRasterPlot.group_trials': True})
TrialToTrialVariabilityComparisonNew(data_store, ParameterSet({'sheet_name1': 'V1_Exc_L4', 'sheet_name2': 'V1_Exc_L2/3', 'data_dg': 0.93, 'data_ni': 1.19}), fig_param={
'dpi': 200, 'figsize': (15, 7.5)}, plot_file_name='TrialToTrialVariabilityComparisonNew.png').plot()
dsv = param_filter_query(
data_store, st_name='FullfieldDriftingSinusoidalGrating')
RasterPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neurons': spike_ids, 'trial_averaged_histogram': False, 'spontaneous': False}), fig_param={
'dpi': 100, 'figsize': (28, 12)}, plot_file_name='EvokedExcRaster.png').plot({'SpikeRasterPlot.group_trials': True})
RasterPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L4', 'neurons': spike_ids_inh, 'trial_averaged_histogram': False, 'spontaneous': False}), fig_param={
'dpi': 100, 'figsize': (28, 12)}, plot_file_name='EvokedInhRaster.png').plot({'SpikeRasterPlot.group_trials': True})
dsv = param_filter_query(
data_store, st_name='FullfieldDriftingSinusoidalGrating', st_orientation=[0, numpy.pi/2])
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neuron': l4_exc, 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="Exc.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L4', 'neuron': l4_inh, 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="Inh.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neuron': analog_ids[0], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="Exc1.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neuron': analog_ids[1], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="Exc2.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neuron': analog_ids[2], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="Exc3.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neuron': analog_ids[3], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="Exc4.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L4', 'neuron': analog_ids_inh[0], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="Inh1.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L4', 'neuron': analog_ids_inh[1], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="Inh2.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L4', 'neuron': analog_ids_inh[2], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="Inh3.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L4', 'neuron': analog_ids_inh[3], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="Inh4.png").plot({'Vm_plot.y_lim': (-80, -50)})
if l23_flag:
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L2/3', 'neuron': analog_ids23[0], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="ExcL231.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L2/3', 'neuron': analog_ids23[1], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="ExcL232.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L2/3', 'neuron': analog_ids23[2], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="ExcL233.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L2/3', 'neuron': analog_ids_inh23[0], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="InhL231.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L2/3', 'neuron': analog_ids_inh23[1], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="InhL232.png").plot({'Vm_plot.y_lim': (-80, -50)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L2/3', 'neuron': analog_ids_inh23[2], 'sheet_activity': {}, 'spontaneous': True}), fig_param={
'dpi': 100, 'figsize': (25, 12)}, plot_file_name="InhL233.png").plot({'Vm_plot.y_lim': (-80, -50)})
if l23_flag:
SpontActOverview(data_store, ParameterSet({'l4_exc_neuron': analog_ids[0], 'l4_inh_neuron': analog_ids_inh[0], 'l23_exc_neuron': analog_ids23[
0], 'l23_inh_neuron': analog_ids_inh23[0]}), plot_file_name='SpontActOverview.png', fig_param={'dpi': 200, 'figsize': (18, 14.5)}).plot()
OrientationTuningSummaryAnalogSignals(data_store, ParameterSet({'exc_sheet_name1': 'V1_Exc_L4', 'inh_sheet_name1': 'V1_Inh_L4', 'exc_sheet_name2': 'V1_Exc_L2/3', 'inh_sheet_name2': 'V1_Inh_L2/3'}), fig_param={
'dpi': 200, 'figsize': (18, 12)}, plot_file_name='OrientationTuningSummaryAnalogSignals.png').plot({'*.fontsize': 19, '*.y_lim': (0, None)})
OrientationTuningSummaryFiringRates(data_store, ParameterSet({'exc_sheet_name1': 'V1_Exc_L4', 'inh_sheet_name1': 'V1_Inh_L4', 'exc_sheet_name2': 'V1_Exc_L2/3', 'inh_sheet_name2': 'V1_Inh_L2/3'}), fig_param={
'dpi': 200, 'figsize': (18, 12)}, plot_file_name='OrientationTuningSummary.png').plot({'*.fontsize': 19})
else:
SpontActOverview(data_store, ParameterSet({'l4_exc_neuron': analog_ids[0], 'l4_inh_neuron': analog_ids_inh[0], 'l23_exc_neuron': -1,
'l23_inh_neuron': -1}), plot_file_name='SpontActOverview.png', fig_param={'dpi': 200, 'figsize': (18, 14.5)}).plot()
OrientationTuningSummaryAnalogSignals(data_store, ParameterSet({'exc_sheet_name1': 'V1_Exc_L4', 'inh_sheet_name1': 'V1_Inh_L4', 'exc_sheet_name2': 'None', 'inh_sheet_name2': 'None'}), fig_param={
'dpi': 200, 'figsize': (18, 12)}, plot_file_name='OrientationTuningSummaryAnalogSignals.png').plot({'*.fontsize': 19, '*.y_lim': (0, None)})
SpontStatisticsOverview(data_store, ParameterSet({}), fig_param={
'dpi': 200, 'figsize': (18, 12)}, plot_file_name='SpontStatisticsOverview.png').plot()
if l23_flag:
MRfigReal(param_filter_query(data_store, sheet_name=['V1_Exc_L2/3', 'V1_Exc_L4', 'V1_Inh_L2/3', 'V1_Inh_L4'], st_contrast=[100], st_name='FullfieldDriftingSinusoidalGrating'), ParameterSet(
{'SimpleSheetName': 'V1_Exc_L4', 'ComplexSheetName': 'V1_Exc_L2/3'}), plot_file_name='MRReal.png', fig_param={'dpi': 100, 'figsize': (19, 12)}).plot()
else:
MRfigReal(param_filter_query(data_store, sheet_name=['V1_Exc_L2/3', 'V1_Exc_L4', 'V1_Inh_L2/3', 'V1_Inh_L4'], st_contrast=[100], st_name='FullfieldDriftingSinusoidalGrating'), ParameterSet(
{'SimpleSheetName': 'V1_Exc_L4', 'ComplexSheetName': 'V1_Exc_L2/3'}), plot_file_name='MRReal.png', fig_param={'dpi': 100, 'figsize': (19, 12)}).plot()
dsv = param_filter_query(
data_store, st_name='NaturalImageWithEyeMovement')
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neuron': l4_exc, 'sheet_activity': {}, 'spontaneous': True}), plot_file_name='NMExc.png', fig_param={
'dpi': 100, 'figsize': (28, 12)}).plot({'Vm_plot.y_lim': (-70, -50), 'Conductance_plot.y_lim': (0, 50.0)})
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L4', 'neuron': l4_inh, 'sheet_activity': {}, 'spontaneous': True}), plot_file_name='NMInh.png', fig_param={
'dpi': 100, 'figsize': (28, 12)}).plot({'Vm_plot.y_lim': (-70, -50), 'Conductance_plot.y_lim': (0, 50.0)})
TrialCrossCorrelationAnalysis(data_store, ParameterSet({'neurons1': list(analog_ids), 'sheet_name1': 'V1_Exc_L4', 'neurons2': list(
analog_ids23), 'sheet_name2': 'V1_Exc_L2/3', 'window_length': 250}), fig_param={"dpi": 100, "figsize": (15, 6.5)}, plot_file_name="trial-to-trial-cross-correlation.png").plot({'*.Vm.title': None, '*.fontsize': 19})
dsv = queries.param_filter_query(data_store, value_name=[
'orientation HWHH of Firing rate', 'orientation CV(Firing rate)'], sheet_name=["V1_Exc_L2/3"], st_contrast=100)
PerNeuronValueScatterPlot(dsv, ParameterSet({'only_matching_units': False, 'ignore_nan': True}), plot_file_name='CVvsHWHH.png').plot(
{'*.x_lim': (0, 90), '*.y_lim': (0, 1.0)})
dsv = param_filter_query(data_store, st_name=['InternalStimulus'])
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Inh_L4', 'neuron': analog_ids_inh[0], 'sheet_activity': {
}, 'spontaneous': False}), fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='SSInhAnalog.png').plot()
# orientation tuning plotting
dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4'],value_name='LGNAfferentOrientation')
PerNeuronValuePlot(dsv,ParameterSet({"cortical_view" : True}),plot_file_name='ORSet.png').plot()
def perform_analysis_and_visualization_stc(data_store):
l23 = 'V1_Exc_L2/3' in set(data_store.sheets())
analog_ids = param_filter_query(data_store, sheet_name="V1_Exc_L4").get_segments()[
0].get_stored_esyn_ids()
analog_ids_inh = param_filter_query(
data_store, sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
spike_ids = param_filter_query(data_store, sheet_name="V1_Exc_L4").get_segments()[
0].get_stored_spike_train_ids()
spike_ids_inh = param_filter_query(data_store, sheet_name="V1_Inh_L4").get_segments()[
0].get_stored_spike_train_ids()
NeuronAnnotationsToPerNeuronValues(data_store, ParameterSet({})).analyse()
if l23:
analog_ids23 = param_filter_query(
data_store, sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_esyn_ids()
analog_ids_inh23 = param_filter_query(
data_store, sheet_name="V1_Inh_L2/3").get_segments()[0].get_stored_esyn_ids()
spike_ids23 = param_filter_query(
data_store, sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_spike_train_ids()
spike_ids_inh23 = param_filter_query(
data_store, sheet_name="V1_Inh_L2/3").get_segments()[0].get_stored_spike_train_ids()
l23_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentOrientation', sheet_name='V1_Exc_L2/3')[0]
l23_exc_or_many = numpy.array(spike_ids23)[numpy.nonzero(numpy.array([circular_dist(
l23_exc_or.get_value_by_id(i), 0, numpy.pi) for i in spike_ids23]) < 0.25)[0]]
idx23 = data_store.get_sheet_indexes(
sheet_name='V1_Exc_L2/3', neuron_ids=l23_exc_or_many)
l4_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentOrientation', sheet_name='V1_Exc_L4')[0]
l4_exc_or_many = numpy.array(spike_ids)[numpy.nonzero(numpy.array([circular_dist(
l4_exc_or.get_value_by_id(i), 0, numpy.pi) for i in spike_ids]) < 0.25)[0]]
idx4 = data_store.get_sheet_indexes(
sheet_name='V1_Exc_L4', neuron_ids=l4_exc_or_many)
x = data_store.get_neuron_positions()['V1_Exc_L4'][0][idx4]
y = data_store.get_neuron_positions()['V1_Exc_L4'][1][idx4]
center4 = l4_exc_or_many[numpy.nonzero(numpy.sqrt(
numpy.multiply(x, x)+numpy.multiply(y, y)) < 0.4)[0]]
analog_center4 = set(center4).intersection(analog_ids)
logger.info(str(analog_center4))
if l23:
x = data_store.get_neuron_positions()['V1_Exc_L2/3'][0][idx23]
y = data_store.get_neuron_positions()['V1_Exc_L2/3'][1][idx23]
center23 = l23_exc_or_many[numpy.nonzero(numpy.sqrt(
numpy.multiply(x, x)+numpy.multiply(y, y)) < 0.4)[0]]
analog_center23 = set(center23).intersection(analog_ids23)
logger.info(str(analog_center23))
if True:
TrialAveragedFiringRate(param_filter_query(data_store, sheet_name=[
'V1_Exc_L4', 'V1_Exc_L2/3'], st_name='DriftingSinusoidalGratingDisk'), ParameterSet({})).analyse()
dsv = param_filter_query(data_store, sheet_name=[
'V1_Exc_L4', 'V1_Exc_L2/3'], st_name='DriftingSinusoidalGratingDisk')
Analog_F0andF1(dsv, ParameterSet({})).analyse()
dsv = param_filter_query(
data_store, st_name='InternalStimulus', st_direct_stimulation_name=None)
Analog_MeanSTDAndFanoFactor(dsv, ParameterSet({})).analyse()
pnv = param_filter_query(data_store, st_name='InternalStimulus', sheet_name='V1_Exc_L4', analysis_algorithm=[
'Analog_MeanSTDAndFanoFactor'], value_name='Mean(VM)', st_direct_stimulation_name=None).get_analysis_result()[0]
dsv = param_filter_query(data_store, st_name='DriftingSinusoidalGratingDisk',
sheet_name='V1_Exc_L4', analysis_algorithm=['Analog_F0andF1'], value_name='F0_Vm')
OperationPNVfromPNVS(pnv, lambda x, y: -(x+y),
'-(x+y)', dsv, ParameterSet({})).analyse()
pnv = param_filter_query(data_store, st_name='InternalStimulus', sheet_name='V1_Exc_L4', analysis_algorithm=[
'Analog_MeanSTDAndFanoFactor'], value_name='Mean(ECond)', st_direct_stimulation_name=None).get_analysis_result()[0]
dsv = param_filter_query(data_store, st_name='DriftingSinusoidalGratingDisk',
sheet_name='V1_Exc_L4', analysis_algorithm=['Analog_F0andF1'], value_name='F0_Exc_Cond')
OperationPNVfromPNVS(pnv, lambda x, y: x-y, 'x-y',
dsv, ParameterSet({})).analyse()
pnv = param_filter_query(data_store, st_name='InternalStimulus', sheet_name='V1_Exc_L4', analysis_algorithm=[
'Analog_MeanSTDAndFanoFactor'], value_name='Mean(ICond)', st_direct_stimulation_name=None).get_analysis_result()[0]
dsv = param_filter_query(data_store, st_name='DriftingSinusoidalGratingDisk',
sheet_name='V1_Exc_L4', analysis_algorithm=['Analog_F0andF1'], value_name='F0_Inh_Cond')
OperationPNVfromPNVS(pnv, lambda x, y: x-y, 'x-y',
dsv, ParameterSet({})).analyse()
if l23:
pnv = param_filter_query(data_store, st_name='InternalStimulus', sheet_name='V1_Exc_L2/3', analysis_algorithm=[
'Analog_MeanSTDAndFanoFactor'], value_name='Mean(VM)', st_direct_stimulation_name=None).get_analysis_result()[0]
dsv = param_filter_query(data_store, st_name='DriftingSinusoidalGratingDisk',
sheet_name='V1_Exc_L2/3', analysis_algorithm=['Analog_F0andF1'], value_name='F0_Vm')
OperationPNVfromPNVS(pnv, lambda x, y: -(x+y),
'-(x+y)', dsv, ParameterSet({})).analyse()
pnv = param_filter_query(data_store, st_name='InternalStimulus', sheet_name='V1_Exc_L2/3', analysis_algorithm=[
'Analog_MeanSTDAndFanoFactor'], value_name='Mean(ECond)', st_direct_stimulation_name=None).get_analysis_result()[0]
dsv = param_filter_query(data_store, st_name='DriftingSinusoidalGratingDisk',
sheet_name='V1_Exc_L2/3', analysis_algorithm=['Analog_F0andF1'], value_name='F0_Exc_Cond')
OperationPNVfromPNVS(pnv, lambda x, y: x-y,
'x-y', dsv, ParameterSet({})).analyse()
pnv = param_filter_query(data_store, st_name='InternalStimulus', sheet_name='V1_Exc_L2/3', analysis_algorithm=[
'Analog_MeanSTDAndFanoFactor'], value_name='Mean(ICond)', st_direct_stimulation_name=None).get_analysis_result()[0]
dsv = param_filter_query(data_store, st_name='DriftingSinusoidalGratingDisk',
sheet_name='V1_Exc_L2/3', analysis_algorithm=['Analog_F0andF1'], value_name='F0_Inh_Cond')
OperationPNVfromPNVS(pnv, lambda x, y: x-y,
'x-y', dsv, ParameterSet({})).analyse()
dsv = param_filter_query(data_store, st_name='DriftingSinusoidalGratingDisk',
analysis_algorithm='TrialAveragedFiringRate', value_name='Firing rate')
SizeTuningAnalysis(dsv, ParameterSet(
{'neurons': center4.tolist(), 'sheet_name': 'V1_Exc_L4'})).analyse()
if l23:
SizeTuningAnalysis(dsv, ParameterSet(
{'neurons': center23.tolist(), 'sheet_name': 'V1_Exc_L2/3'})).analyse()
data_store.save()
dsv = param_filter_query(data_store, st_name='DriftingSinusoidalGratingDisk', analysis_algorithm=[
'TrialAveragedFiringRate'], value_name="Firing rate")
PlotTuningCurve(dsv, ParameterSet({'parameter_name': 'radius', 'neurons': list(center4), 'sheet_name': 'V1_Exc_L4', 'centered': False,
'mean': False, 'polar': False, 'pool': False}), plot_file_name='SizeTuningExcL4.png', fig_param={'dpi': 100, 'figsize': (32, 7)}).plot()
PlotTuningCurve(dsv, ParameterSet({'parameter_name': 'radius', 'neurons': list(center4), 'sheet_name': 'V1_Exc_L4', 'centered': False,
'mean': True, 'polar': False, 'pool': False}), plot_file_name='SizeTuningExcL4M.png', fig_param={'dpi': 100, 'figsize': (32, 7)}).plot()
if l23:
PlotTuningCurve(dsv, ParameterSet({'parameter_name': 'radius', 'neurons': list(center23), 'sheet_name': 'V1_Exc_L2/3', 'centered': False,
'mean': False, 'polar': False, 'pool': False}), plot_file_name='SizeTuningExcL23.png', fig_param={'dpi': 100, 'figsize': (32, 7)}).plot()
PlotTuningCurve(dsv, ParameterSet({'parameter_name': 'radius', 'neurons': list(center23), 'sheet_name': 'V1_Exc_L2/3', 'centered': False,
'mean': True, 'polar': False, 'pool': False}), plot_file_name='SizeTuningExcL23M.png', fig_param={'dpi': 100, 'figsize': (32, 7)}).plot()
if True:
dsv = param_filter_query(data_store, st_name=[
'DriftingSinusoidalGratingDisk'])
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neuron': list(analog_center4)[0], 'sheet_activity': {
}, 'spontaneous': True}), fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='Overview_ExcL4_1.png').plot()
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L2/3', 'neuron': list(analog_center23)[0], 'sheet_activity': {
}, 'spontaneous': True}), fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='Overview_ExcL23_1.png').plot()
if l23:
SizeTuningOverview(data_store, ParameterSet({'l4_neurons': list(center4), 'l23_neurons': list(center23), 'l4_neurons_analog': list(
analog_center4), 'l23_neurons_analog': list(analog_center23)}), plot_file_name='SizeTuningOverview.png', fig_param={'dpi': 300, 'figsize': (18, 8)}).plot()
SizeTuningOverviewNew(data_store, ParameterSet({'l4_neurons': list(center4), 'l23_neurons': list(center23), 'l4_neurons_analog': list(
analog_center4), 'l23_neurons_analog': list(analog_center23)}), plot_file_name='SizeTuningOverviewNew.png', fig_param={'dpi': 300, 'figsize': (18, 8)}).plot()
else:
SizeTuningOverview(data_store, ParameterSet({'l4_neurons': list(center4), 'l23_neurons': [], 'l4_neurons_analog': list(
analog_center4), 'l23_neurons_analog': []}), plot_file_name='SizeTuningOverview.png', fig_param={'dpi': 300, 'figsize': (18, 8)}).plot()
SizeTuningOverviewNew(data_store, ParameterSet({'l4_neurons': list(center4), 'l23_neurons': [], 'l4_neurons_analog': list(
analog_center4), 'l23_neurons_analog': []}), plot_file_name='SizeTuningOverviewNew.png', fig_param={'dpi': 300, 'figsize': (18, 8)}).plot()
if True:
dsv = param_filter_query(data_store, st_name=[
'DriftingSinusoidalGratingDisk'], st_size=[5.0])
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neuron': list(analog_center4)[0], 'sheet_activity': {
}, 'spontaneous': True}), fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='Overview_ExcL4_Small1.png').plot()
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neuron': list(analog_center4)[1], 'sheet_activity': {
}, 'spontaneous': True}), fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='Overview_ExcL4_Small2.png').plot()
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neuron': list(analog_center4)[2], 'sheet_activity': {
}, 'spontaneous': True}), fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='Overview_ExcL4_Small3.png').plot()
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L2/3', 'neuron': list(analog_center23)[0], 'sheet_activity': {
}, 'spontaneous': True}), fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='Overview_ExcL23_Small1.png').plot()
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L2/3', 'neuron': list(analog_center23)[1], 'sheet_activity': {
}, 'spontaneous': True}), fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='Overview_ExcL23_Small2.png').plot()
OverviewPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L2/3', 'neuron': list(analog_center23)[2], 'sheet_activity': {
}, 'spontaneous': True}), fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='Overview_ExcL23_Small3.png').plot()
RasterPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neurons': spike_ids, 'trial_averaged_histogram': False, 'spontaneous': False}), fig_param={
'dpi': 100, 'figsize': (28, 12)}, plot_file_name='EvokedExcRasterL4.png').plot({'SpikeRasterPlot.group_trials': True})
RasterPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L2/3', 'neurons': spike_ids, 'trial_averaged_histogram': False, 'spontaneous': False}),
fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='EvokedExcRasterL2/3.png').plot({'SpikeRasterPlot.group_trials': True})
dsv = param_filter_query(data_store, st_name=['InternalStimulus'])
RasterPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L4', 'neurons': spike_ids, 'trial_averaged_histogram': False, 'spontaneous': False}), fig_param={
'dpi': 100, 'figsize': (28, 12)}, plot_file_name='SSExcRasterL4.png').plot({'SpikeRasterPlot.group_trials': True})
RasterPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L2/3', 'neurons': spike_ids, 'trial_averaged_histogram': False, 'spontaneous': False}),
fig_param={'dpi': 100, 'figsize': (28, 12)}, plot_file_name='SSExcRasterL2/3.png').plot({'SpikeRasterPlot.group_trials': True})
def LFP(sheet,
folder,
stimulus,
parameter,
tip=[.0, .0, .0],
sigma=0.300,
ylim=[0., -1.],
addon="",
color='black'):
import matplotlib as ml
import quantities as pq
print inspect.stack()[0][3]
print "folder: ", folder
print "sheet: ", sheet
data_store = PickledDataStore(load=True,
parameters=ParameterSet({
'root_directory':
folder,
'store_stimuli':
False
}),
replace=True)
data_store.print_content(full_recordings=False)
ids = param_filter_query(
data_store, sheet_name=sheet,
st_name=stimulus).get_segments()[0].get_stored_esyn_ids()
if ids == None or len(ids) < 1:
print "No gesyn recorded.\n"
return
print "Recorded gesyn:", len(ids), ids
ids = param_filter_query(
data_store, sheet_name=sheet,
st_name=stimulus).get_segments()[0].get_stored_vm_ids()
if ids == None or len(ids) < 1:
print "No Vm recorded.\n"
return
print "Recorded Vm:", len(ids), ids
NeuronAnnotationsToPerNeuronValues(data_store, ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name=sheet)[0]
l4_exc_or_many = numpy.array(ids)[numpy.nonzero(
numpy.array([
circular_dist(l4_exc_or.get_value_by_id(i), 0, numpy.pi)
for i in ids
]) < .1)[0]]
ids = list(l4_exc_or_many)
print "Recorded neurons:", len(ids), ids
# 900 neurons over 6000 micrometers, 200 micrometers interval
sheet_indexes = data_store.get_sheet_indexes(sheet_name=sheet,
neuron_ids=ids)
positions = data_store.get_neuron_postions()[sheet]
print positions.shape # all 10800
# take the positions of the ids
ids_positions = numpy.transpose(positions)[sheet_indexes, :]
print ids_positions.shape
print ids_positions
# Pre-compute distances from the LFP tip
distances = []
for i in range(len(ids)):
distances.append(
numpy.linalg.norm(
numpy.array(ids_positions[i][0]) - numpy.array(tip)))
distances = numpy.array(distances)
print "distances:", len(distances), distances
# ##############################
# LFP
# tip = [[x],[y],[.0]]
# For each recorded cell:
# Gaussianly weight it by its distance from tip
# produce the currents
# Divide the whole by the norm factor (area): 4 * numpy.pi * sigma
# 95% of the LFP signal is a result of all exc and inh cells conductances from 250um radius from the tip of the electrode (Katzner et al. 2009).
# Mostly excitatory neurons are relevant for the LFP (because of their geometry) Bartos
# Therefore we include all recorded cells but account for the distance-dependent contribution weighting currents /r^2
# We assume that the electrode has been placed in the cortical coordinates <tip>
# Given that the current V1 orientation map has a pixel for each 100 um, a reasonable way to look at a neighborhood is in a radius of 300 um
print "LFP electrode tip location (x,y) in degrees:", tip
# Gather vm and conductances
segs = sorted(
param_filter_query(data_store, st_name=stimulus,
sheet_name=sheet).get_segments(),
key=lambda x: getattr(
MozaikParametrized.idd(x.annotations['stimulus']), parameter))
ticks = set([])
for x in segs:
ticks.add(
getattr(MozaikParametrized.idd(x.annotations['stimulus']),
parameter))
ticks = sorted(ticks)
num_ticks = len(ticks)
print ticks
trials = len(segs) / num_ticks
print "trials:", trials
pop_vm = []
pop_gsyn_e = []
pop_gsyn_i = []
for n, idd in enumerate(ids):
print "idd", idd
full_vm = [s.get_vm(idd) for s in segs] # all segments
full_gsyn_es = [s.get_esyn(idd) for s in segs]
full_gsyn_is = [s.get_isyn(idd) for s in segs]
print "len full_gsyn_e", len(
full_gsyn_es) # segments = stimuli * trials
print "shape gsyn_e[0]", full_gsyn_es[0].shape # stimulus lenght
# mean input over trials
mean_full_vm = numpy.zeros((num_ticks, full_vm[0].shape[0])) # init
mean_full_gsyn_e = numpy.zeros(
(num_ticks, full_gsyn_es[0].shape[0])) # init
mean_full_gsyn_i = numpy.zeros((num_ticks, full_gsyn_es[0].shape[0]))
# print "shape mean_full_gsyn_e/i", mean_full_gsyn_e.shape
sampling_period = full_gsyn_es[0].sampling_period
t_stop = float(full_gsyn_es[0].t_stop - sampling_period) # 200.0
t_start = float(full_gsyn_es[0].t_start)
time_axis = numpy.arange(0, len(full_gsyn_es[0]), 1) / float(
len(full_gsyn_es[0])) * abs(t_start - t_stop) + t_start
# sum by size
t = 0
for v, e, i in zip(full_vm, full_gsyn_es, full_gsyn_is):
s = int(t / trials)
v = v.rescale(mozaik.tools.units.mV)
e = e.rescale(
mozaik.tools.units.nS) # NEST is in nS, PyNN is in uS
i = i.rescale(
mozaik.tools.units.nS) # NEST is in nS, PyNN is in uS
mean_full_vm[s] = mean_full_vm[s] + numpy.array(v.tolist())
mean_full_gsyn_e[s] = mean_full_gsyn_e[s] + numpy.array(e.tolist())
mean_full_gsyn_i[s] = mean_full_gsyn_i[s] + numpy.array(i.tolist())
t = t + 1
# average by trials
for st in range(num_ticks):
mean_full_vm[st] = mean_full_vm[st] / trials
mean_full_gsyn_e[st] = mean_full_gsyn_e[st] / trials
mean_full_gsyn_i[st] = mean_full_gsyn_i[st] / trials
pop_vm.append(mean_full_vm)
pop_gsyn_e.append(mean_full_gsyn_e)
pop_gsyn_i.append(mean_full_gsyn_i)
pop_v = numpy.array(pop_vm)
pop_e = numpy.array(pop_gsyn_e)
pop_i = numpy.array(pop_gsyn_i)
# Produce the current for each cell for this time interval, with the Ohm law:
# I = ge(V-Ee) + gi(V+Ei)
# where
# Ee is the equilibrium for exc, which is 0.0
# Ei is the equilibrium for inh, which is -80.0
i = pop_e * (pop_v - 0.0) + pop_i * (pop_v - 80.0)
# i = pop_e*(pop_v-0.0) + 0.3*pop_i*(pop_v-80.0)
# i = pop_e*(pop_v-0.0) # only exc
# the LFP is the result of cells' currents divided by the distance
sum_i = numpy.sum(i, axis=0)
lfp = sum_i / (4 * numpy.pi * sigma) #
lfp /= 1000. # from milli to micro
print "LFP:", lfp.shape, lfp.mean(), lfp.min(), lfp.max()
# print lfp
# lfp = np.convolve(lfp, np.ones((10,))/10, mode='valid') # moving avg or running mean implemented as a convolution over steps of 10, divided by 10
# lfp = np.convolve(lfp, np.ones((10,))/10, mode='valid') # moving avg or running mean implemented as a convolution over steps of 10, divided by 10
#plot the LFP for each stimulus
for s in range(num_ticks):
# for each stimulus plot the average conductance per cell over time
matplotlib.rcParams.update({'font.size': 22})
fig, ax = plt.subplots()
ax.plot(range(0, len(lfp[s])), lfp[s], color=color, linewidth=3)
# ax.set_ylim([lfp.min(), lfp.max()])
ax.set_ylim(ylim)
ax.set_ylabel("LFP (uV)")
ax.set_xlabel("Time (us)")
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
ax.xaxis.set_ticks(ticks, ticks)
ax.yaxis.set_ticks_position('left')
# text
plt.tight_layout()
plt.savefig(folder + "/TimecourseLFP_" + sheet + "_" + parameter +
"_" + str(ticks[s]) + "_" + addon + ".svg",
dpi=200,
transparent=True)
fig.clf()
plt.close()
# garbage
gc.collect()
def perform_analysis_and_visualization(data_store,gratings,cort_stim,nat_stim,tp,scale=True,sharpness=False,lee_size=False):
scales = [0.01,0.07,0.14,1.0];
sheets = list(set(data_store.sheets()) & set(['V1_Exc_L4','V1_Inh_L4']))
exc_sheets = list(set(data_store.sheets()) & set(['V1_Exc_L4']))
NeuronAnnotationsToPerNeuronValues(data_store,ParameterSet({})).analyse()
analog_ids = param_filter_query(data_store,sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
analog_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
analog_ids23 = param_filter_query(data_store,sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_esyn_ids()
analog_ids_inh23 = param_filter_query(data_store,sheet_name="V1_Inh_L2/3").get_segments()[0].get_stored_esyn_ids()
spike_ids = param_filter_query(data_store,sheet_name="V1_Exc_L4").get_segments()[0].get_stored_spike_train_ids()
spike_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L4").get_segments()[0].get_stored_spike_train_ids()
spike_ids23 = param_filter_query(data_store,sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_spike_train_ids()
spike_ids_inh23 = param_filter_query(data_store,sheet_name="V1_Inh_L2/3").get_segments()[0].get_stored_spike_train_ids()
l4_exc_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Exc_L4')
l4_exc_phase = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentPhase', sheet_name = 'V1_Exc_L4')
l4_exc = analog_ids[numpy.argmin([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_exc_or[0].get_value_by_id(analog_ids),l4_exc_phase[0].get_value_by_id(analog_ids))])]
l4_inh_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Inh_L4')
l4_inh_phase = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentPhase', sheet_name = 'V1_Inh_L4')
l4_inh = analog_ids_inh[numpy.argmin([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_inh_or[0].get_value_by_id(analog_ids_inh),l4_inh_phase[0].get_value_by_id(analog_ids_inh))])]
l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(numpy.array([circular_dist(o,0,numpy.pi) for (o,p) in zip(l4_exc_or[0].values,l4_exc_phase[0].values)]) < 0.1)[0]]
l4_exc_or_many = list(set(l4_exc_or_many) & set(spike_ids))
l23_exc_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Exc_L2/3')[0]
l23_inh_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Inh_L2/3')[0]
l23_exc_or_many = numpy.array(spike_ids23)[numpy.nonzero(numpy.array([circular_dist(l23_exc_or.get_value_by_id(i),0,numpy.pi) for i in spike_ids23]) < 0.1)[0]]
l23_exc_or_many_analog = numpy.array(analog_ids23)[numpy.nonzero(numpy.array([circular_dist(l23_exc_or.get_value_by_id(i),0,numpy.pi) for i in analog_ids23]) < 0.1)[0]]
l23_exc_or_many_analog_inh = numpy.array(analog_ids_inh23)[numpy.nonzero(numpy.array([circular_dist(l23_inh_or.get_value_by_id(i),0,numpy.pi) for i in analog_ids_inh23]) < 0.1)[0]]
l23_exc_or_many = list(set(l23_exc_or_many) & set(spike_ids23))
orr = list(set([MozaikParametrized.idd(s).orientation for s in queries.param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGratingA',st_contrast=100).get_stimuli()]))
l4_exc_or_many_analog = numpy.array(analog_ids)[numpy.nonzero(numpy.array([circular_dist(l4_exc_or[0].get_value_by_id(i),0,numpy.pi) for i in analog_ids]) < 0.1)[0]]
l4_inh_or_many_analog = numpy.array(analog_ids_inh)[numpy.nonzero(numpy.array([circular_dist(l4_inh_or[0].get_value_by_id(i),0,numpy.pi) for i in analog_ids_inh]) < 0.15)[0]]
lgn_on_ids = param_filter_query(data_store,sheet_name="X_ON").get_segments()[0].get_stored_spike_train_ids()
lgn_off_ids = param_filter_query(data_store,sheet_name="X_OFF").get_segments()[0].get_stored_spike_train_ids()
analysis(data_store,analog_ids23,l23_exc_or_many,analog_ids_inh23,gratings,cort_stim,scale)
data_store.save()
def overviews(dsv,name_prefix):
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : analog_ids23[0], 'sheet_activity' : {}, 'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'L23ExcAnalog1.png').plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : analog_ids_inh23[0], 'sheet_activity' : {}, 'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'L23InhAnalog1.png').plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : analog_ids23[1], 'sheet_activity' : {}, 'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'L23ExcAnalog2.png').plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : analog_ids_inh23[1], 'sheet_activity' : {}, 'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'L23InhAnalog2.png').plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : analog_ids23[2], 'sheet_activity' : {}, 'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'L23ExcAnalog3.png').plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : analog_ids_inh23[2], 'sheet_activity' : {}, 'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'L23InhAnalog3.png').plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[0], 'sheet_activity' : {}, 'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'L4ExcAnalog1.png').plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : analog_ids_inh[0], 'sheet_activity' : {}, 'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'L4InhAnalog1.png').plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[1], 'sheet_activity' : {}, 'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'L4ExcAnalog2.png').plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : analog_ids_inh[1], 'sheet_activity' : {}, 'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'L4InhAnalog2.png').plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[2], 'sheet_activity' : {}, 'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'L4ExcAnalog3.png').plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : analog_ids_inh[2], 'sheet_activity' : {}, 'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'L4InhAnalog3.png').plot()
RasterPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neurons' : spike_ids,'trial_averaged_histogram': False, 'spontaneous' : False}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'SSExcRasterL4.png').plot({'SpikeRasterPlot.group_trials':True})
RasterPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neurons' : spike_ids_inh,'trial_averaged_histogram': False, 'spontaneous' : False}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'SSInhRasterL4.png').plot({'SpikeRasterPlot.group_trials':True})
RasterPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neurons' : spike_ids23,'trial_averaged_histogram': False, 'spontaneous' : False}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'SSExcRasterL23.png').plot({'SpikeRasterPlot.group_trials':True})
RasterPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neurons' : spike_ids_inh23,'trial_averaged_histogram': False, 'spontaneous' : False}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'SSInhRasterL23.png').plot({'SpikeRasterPlot.group_trials':True})
RasterPlot(dsv,ParameterSet({'sheet_name' : 'X_ON', 'neurons' : lgn_on_ids,'trial_averaged_histogram': False, 'spontaneous' : False}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'XONRaster.png').plot({'SpikeRasterPlot.group_trials':True})
RasterPlot(dsv,ParameterSet({'sheet_name' : 'X_OFF', 'neurons' : lgn_off_ids,'trial_averaged_histogram': False, 'spontaneous' : False}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name=name_prefix+'XOFFRasterL4.png').plot({'SpikeRasterPlot.group_trials':True})
# spontaneous activity overview
dsv = param_filter_query(data_store,st_name='InternalStimulus',st_direct_stimulation_name=None)
overviews(dsv,"SPONT_")
x = data_store.get_neuron_postions()['V1_Exc_L2/3'][0]
y = data_store.get_neuron_postions()['V1_Exc_L2/3'][1]
depth = data_store.get_neuron_postions()['V1_Exc_L2/3'][2]
ors = numpy.array(l23_exc_or.ids)[numpy.nonzero(numpy.array([circular_dist(o,0,numpy.pi) for o in l23_exc_or.values]) < 0.15)[0]]
close_spikes = numpy.array([a for a in ors if (a in spike_ids23)])
close_spikes_sheet_indexes = data_store.get_sheet_indexes(sheet_name='V1_Exc_L2/3',neuron_ids=close_spikes)
close_spikes = close_spikes[numpy.sqrt(x[close_spikes_sheet_indexes]**2 + y[close_spikes_sheet_indexes]**2)<1.5]
depth_of_close_spikes = depth[data_store.get_sheet_indexes(sheet_name='V1_Exc_L2/3',neuron_ids=close_spikes)]
depth_of_close_spikes, close_spikes = zip(*sorted(zip(depth_of_close_spikes,close_spikes),reverse=True))
close_analogs = list(set(close_spikes) & set(analog_ids23))
if cort_stim:
if sharpness:
OrientationTuningSharpness(data_store,ParameterSet({}),fig_param={'dpi' : 300,'figsize': (13,10)},plot_file_name='Sharpness.png').plot()
return;
if lee_size:
OrientationTuningLEE_size(data_store,ParameterSet({}),fig_param={'dpi' : 300,'figsize': (13,10)},plot_file_name='LEESizeFigure.png').plot()
return;
if scale:
ContrastResponse(data_store,ParameterSet({'cortical_stimulation' : True}),fig_param={'dpi' : 100,'figsize': (9,3.6)},plot_file_name='ContrastResponse.png').plot()
else:
ContrastResponseTransformed(data_store,ParameterSet({'cortical_stimulation' : True, 'type' : tp }),fig_param={'dpi' : 100,'figsize': (9,3.6)},plot_file_name='ContrastResponseTransformed.png').plot()
if scale:
dsv = param_filter_query(data_store,st_name='InternalStimulus',st_direct_stimulation_name="LocalStimulatorArray")
LightStimulationOverview(data_store,ParameterSet({}),fig_param={'dpi' : 300,'figsize': (18,6.9)},plot_file_name='LightStimulationOverview.png').plot({'*.SpikeRasterPlot.group_trials':True,'*.SpikeRasterPlot.x_label':'neuron #','*.title' : None,'Conductance_plot.y_lim' : (0,170)})
if scale:
dsv = queries.param_filter_query(data_store,st_name='InternalStimulus',st_direct_stimulation_name='LocalStimulatorArray',st_stimulating_signal_parameters_scale = [0.08],st_stimulating_signal_parameters_orientation = 0)
else:
dsv = queries.param_filter_query(data_store,st_name='InternalStimulus',st_direct_stimulation_name='LocalStimulatorArray',st_stimulating_signal_parameters_contrast = [100],st_stimulating_signal_parameters_orientation = 0)
ResponseOverview(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : close_analogs[12] }),fig_param={'dpi' : 100,'figsize': (16,2.5)},plot_file_name='ResponseOverview.png').plot({ 'Conductance_plot.y_lim' : (0,150)})
overviews(dsv,"CS_")
if scale:
OrientationTuningSummaryFiringRates(data_store,ParameterSet({'cortical_stimulation' : True, 'scale' : True}),plot_file_name='OrientationTuning.png',fig_param={'dpi' : 100,'figsize': (18,3.6)}).plot()
else:
OrientationTuningSummaryFiringRates(data_store,ParameterSet({'cortical_stimulation' : True, 'scale' : False}),plot_file_name='OrientationTuning.png',fig_param={'dpi' : 100,'figsize': (18,3.6)}).plot()
StatisticsOverview(data_store,ParameterSet({'cortical_stimulation' : True ,'type' : tp, 'window_length': 200}),plot_file_name='StatisticsOverview.png',fig_param={'dpi' : 100,'figsize': (12,8)}).plot()
if gratings:
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGratingA',st_orientation=[0,numpy.pi/2],st_contrast=100)
overviews(dsv,"GR_")
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGratingA',st_orientation=[0],st_contrast=100)
ResponseOverview(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : close_analogs[12] }),fig_param={'dpi' : 100,'figsize': (16,2.5)},plot_file_name='ResponseOverview.png').plot({ 'Conductance_plot.y_lim' : (0,150)})
ContrastResponseTransformed(data_store,ParameterSet({'cortical_stimulation' : False, 'type' : tp }),fig_param={'dpi' : 100,'figsize': (9,3.6)},plot_file_name='ContrastResponseTransformed.png').plot()
OrientationTuningSummaryFiringRates(data_store,ParameterSet({'cortical_stimulation' : False, 'scale' : False}),plot_file_name='OrientationTuning.png',fig_param={'dpi' : 100,'figsize': (18,3.6)}).plot()
StatisticsOverview(data_store,ParameterSet({'cortical_stimulation' : False ,'type' : tp, 'window_length': 50}),plot_file_name='StatisticsOverview.png',fig_param={'dpi' : 100,'figsize': (12,8)}).plot()
if False:
pref_fr_100 = param_filter_query(data_store,sheet_name=['V1_Exc_L4'],st_contrast=[100],analysis_algorithm=['TrialAveragedFiringRate'],value_name='Firing rate',st_orientation=[0],ads_unique=True).get_analysis_result()[0].get_value_by_id(l4_exc_or_many)
ort_fr_100 = param_filter_query(data_store,sheet_name=['V1_Exc_L4'],st_contrast=[100],analysis_algorithm=['TrialAveragedFiringRate'],value_name='Firing rate',st_orientation=[numpy.pi/2],ads_unique=True).get_analysis_result()[0].get_value_by_id(l4_exc_or_many)
pref_fr_50 = 0
ort_fr_50 = 0
if len(param_filter_query(data_store,sheet_name=['V1_Exc_L4'],st_contrast=[5],analysis_algorithm=['TrialAveragedFiringRate'],value_name='Firing rate',st_orientation=[0]).get_analysis_result()) != 0:
pref_fr_50 = param_filter_query(data_store,sheet_name=['V1_Exc_L4'],st_contrast=[5],analysis_algorithm=['TrialAveragedFiringRate'],value_name='Firing rate',st_orientation=[0],ads_unique=True).get_analysis_result()[0].get_value_by_id(l4_exc_or_many)
ort_fr_50 = param_filter_query(data_store,sheet_name=['V1_Exc_L4'],st_contrast=[5],analysis_algorithm=['TrialAveragedFiringRate'],value_name='Firing rate',st_orientation=[numpy.pi/2],ads_unique=True).get_analysis_result()[0].get_value_by_id(l4_exc_or_many)
spont = param_filter_query(data_store,st_name='InternalStimulus',sheet_name=['V1_Exc_L4'],analysis_algorithm=['TrialAveragedFiringRate'],value_name='Firing rate',ads_unique=True).get_analysis_result()[0].get_value_by_id(l4_exc_or_many)
pylab.figure()
pylab.bar([1,2,3,4,5],[numpy.mean(pref_fr_100),numpy.mean(ort_fr_100),numpy.mean(pref_fr_50),numpy.mean(ort_fr_50),numpy.mean(spont)])
pylab.xticks([1.4,2.4,3.4,4.4,5.4],['PREF100','ORT100','PREF50','ORT50','SPONT'])
pylab.xlim([0.8,6.0])
pylab.ylabel("Firing rate")
pylab.savefig(Global.root_directory+"Orientation_responseL4.png")
if True:
pref_fr_100 = param_filter_query(data_store,sheet_name=['V1_Exc_L2/3'],st_contrast=[100],analysis_algorithm=['TrialAveragedFiringRate'],value_name='Firing rate',st_orientation=[0],ads_unique=True).get_analysis_result()[0].get_value_by_id(l23_exc_or_many)
ort_fr_100 = param_filter_query(data_store,sheet_name=['V1_Exc_L2/3'],st_contrast=[100],analysis_algorithm=['TrialAveragedFiringRate'],value_name='Firing rate',st_orientation=[numpy.pi/2],ads_unique=True).get_analysis_result()[0].get_value_by_id(l23_exc_or_many)
pref_fr_50 = 0
ort_fr_50 = 0
if(len(param_filter_query(data_store,sheet_name=['V1_Exc_L2/3'],st_contrast=[5],analysis_algorithm=['TrialAveragedFiringRate'],value_name='Firing rate',st_orientation=[0]).get_analysis_result())!=0):
pref_fr_50 = param_filter_query(data_store,sheet_name=['V1_Exc_L2/3'],st_contrast=[5],analysis_algorithm=['TrialAveragedFiringRate'],value_name='Firing rate',st_orientation=[0],ads_unique=True).get_analysis_result()[0].get_value_by_id(l23_exc_or_many)
ort_fr_50 = param_filter_query(data_store,sheet_name=['V1_Exc_L2/3'],st_contrast=[5],analysis_algorithm=['TrialAveragedFiringRate'],value_name='Firing rate',st_orientation=[numpy.pi/2],ads_unique=True).get_analysis_result()[0].get_value_by_id(l23_exc_or_many)
spont = param_filter_query(data_store,st_name='InternalStimulus',sheet_name=['V1_Exc_L2/3'],analysis_algorithm=['TrialAveragedFiringRate'],value_name='Firing rate',ads_unique=True).get_analysis_result()[0].get_value_by_id(l23_exc_or_many)
pylab.figure()
pylab.bar([1,2,3,4,5],[numpy.mean(pref_fr_100),numpy.mean(ort_fr_100),numpy.mean(pref_fr_50),numpy.mean(ort_fr_50),numpy.mean(spont)])
pylab.xticks([1.4,2.4,3.4,4.4,5.4],['PREF100','ORT100','PREF50','ORT50','SPONT'])
pylab.xlim([0.8,6.0])
pylab.ylabel("Firing rate")
pylab.savefig(Global.root_directory+"Orientation_responseL23.png")
SpontStatisticsOverview(data_store,ParameterSet({}), fig_param={'dpi' : 200,'figsize': (12,8)},plot_file_name='SpontStatisticsOverview.png').plot()
SpontActOverview(data_store,ParameterSet({'l4_exc_neuron' : analog_ids[0], 'l4_inh_neuron' : analog_ids_inh[0],'l23_exc_neuron' : analog_ids23[0],'l23_inh_neuron' : analog_ids_inh23[0]}),plot_file_name='SpontActOverview.png', fig_param={'dpi' : 200,'figsize': (18,14.5)}).plot()
dsv = param_filter_query(data_store,sheet_name = 'V1_Exc_L4')
MeanVsVarainceOfVM(dsv,ParameterSet({'neurons' : list(analog_ids[:5])}),fig_param={'dpi' : 100,'figsize': (15,7.5)},plot_file_name='TrialToTrialMeanVsVarianceOfVM.png').plot()
dsv = queries.param_filter_query(data_store,value_name=['orientation HWHH of Firing rate','orientation CV(Firing rate)'],sheet_name=["V1_Exc_L2/3"],st_contrast=100)
PerNeuronValueScatterPlot(dsv, ParameterSet({'only_matching_units' : False,'ignore_nan' : True}),plot_file_name='CVvsHWHH.png').plot({'*.x_lim' : (0,90),'*.y_lim' : (0,1.0)})
MRfig(param_filter_query(data_store,sheet_name=['V1_Exc_L2/3','V1_Exc_L4'],st_contrast=[100],st_name='FullfieldDriftingSinusoidalGratingA'),ParameterSet({'SimpleSheetName' : 'V1_Exc_L4','ComplexSheetName' : 'V1_Exc_L2/3'}),plot_file_name='MR.png',fig_param={'dpi' : 100,'figsize': (19,12)}).plot()
MRfigReal(param_filter_query(data_store,sheet_name=['V1_Exc_L2/3','V1_Exc_L4'],st_contrast=[100],st_name='FullfieldDriftingSinusoidalGratingA'),ParameterSet({'SimpleSheetName' : 'V1_Exc_L4','ComplexSheetName' : 'V1_Exc_L2/3'}),plot_file_name='MRReal.png',fig_param={'dpi' : 100,'figsize': (19,12)}).plot()
dsv = param_filter_query(data_store,st_name='InternalStimulus',st_direct_stimulation_name='LocalStimulatorArray',st_stimulating_signal_parameters_orientation=0,st_stimulating_signal_parameters_scale=1.0)
ActivityMovie(dsv,ParameterSet({'bin_width': 20.0,'scatter': True,'resolution': 10,'sheet_name': 'V1_Exc_L2/3', 'exp_time_constant': 200}),fig_param={'dpi' : 100,'figsize': (12,6)},plot_file_name='cs_or_0_l23').plot({'*.title' : None})
if nat_stim:
dsv = param_filter_query(data_store,st_name='NaturalImageWithEyeMovement')
overviews(dsv,"NI_")
TrialToTrialVariabilityComparison(data_store,ParameterSet({'sheet_name1' : 'V1_Exc_L4','sheet_name2' : 'V1_Exc_L4','data_dg' : 0.93 , 'data_ni' : 1.19}),fig_param={'dpi' : 200,'figsize': (15,7.5)},plot_file_name='TrialToTrialVariabilityComparison.png').plot()
dsv = param_filter_query(data_store,st_name='NaturalImageWithEyeMovement')
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}, 'spontaneous' : True}),plot_file_name='NMExc.png',fig_param={'dpi' : 100,'figsize': (28,12)}).plot({'Vm_plot.y_lim' : (-70,-50),'Conductance_plot.y_lim' : (0,50.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}, 'spontaneous' : True}),plot_file_name='NMInh.png',fig_param={'dpi' : 100,'figsize': (28,12)}).plot({'Vm_plot.y_lim' : (-70,-50),'Conductance_plot.y_lim' : (0,50.0)})
TrialCrossCorrelationAnalysis(data_store,ParameterSet({'neurons1' : list(analog_ids),'sheet_name1' : 'V1_Exc_L4','neurons2' : list(analog_ids23),'sheet_name2' : 'V1_Exc_L2/3', 'window_length' : 250}),fig_param={"dpi" : 100,"figsize": (15,6.5)},plot_file_name="trial-to-trial-cross-correlation.png").plot({'*.Vm.title' : None, '*.fontsize' : 19})
def trial_averaged_tuning_curve_errorbar(sheet,
folder,
stimulus,
parameter,
start,
end,
xlabel="",
ylabel="",
color="black",
percentile=False,
useXlog=False,
useYlog=False,
ylim=[0., 100.],
xlim=False,
opposite=False,
box=None,
radius=None,
addon="",
data=None,
data_curve=True):
print inspect.stack()[0][3]
print "folder: ", folder
print "sheet: ", sheet
data_store = PickledDataStore(load=True,
parameters=ParameterSet({
'root_directory':
folder,
'store_stimuli':
False
}),
replace=True)
data_store.print_content(full_recordings=False)
neurons = []
neurons = param_filter_query(
data_store, sheet_name=sheet,
st_name=stimulus).get_segments()[0].get_stored_spike_train_ids()
print "Recorded neurons:", len(neurons)
if radius:
sheet_ids = data_store.get_sheet_indexes(sheet_name=sheet,
neuron_ids=neurons)
positions = data_store.get_neuron_postions()[sheet]
if radius:
ids1 = select_ids_by_position(positions, sheet_ids, radius=radius)
neurons = data_store.get_sheet_ids(sheet_name=sheet, indexes=ids1)
NeuronAnnotationsToPerNeuronValues(data_store, ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name=sheet)[0]
l4_exc_or_many = numpy.array(neurons)[numpy.nonzero(
numpy.array([
circular_dist(l4_exc_or.get_value_by_id(i), 0, numpy.pi)
for i in neurons
]) < .1)[0]]
neurons = list(l4_exc_or_many)
print "Selected neurons:", len(neurons) #, neurons
if len(neurons) < 1:
return
TrialAveragedFiringRate(
param_filter_query(data_store, sheet_name=sheet, st_name=stimulus),
ParameterSet({'neurons': list(neurons)})).analyse()
PlotTuningCurve(
param_filter_query(data_store,
st_name=stimulus,
analysis_algorithm=['TrialAveragedFiringRate']),
ParameterSet({
'polar': False,
'pool': False,
'centered': False,
'percent': False,
'mean': True,
'parameter_name': parameter,
'neurons': list(neurons),
'sheet_name': sheet
}),
fig_param={
'dpi': 200
},
plot_file_name=folder + "/TrialAveragedSensitivityNew_" + stimulus +
"_" + parameter + "_" + str(sheet) + "_" + addon + "_mean.svg"
).plot({
# '*.y_lim':(0,30),
# '*.x_lim':(-10,100),
# '*.x_scale':'log', '*.x_scale_base':10,
'*.fontsize': 17
})
return
def perform_analysis_and_visualization_or(data_store):
analog_ids = param_filter_query(
data_store,
sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
analog_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
spike_ids = param_filter_query(
data_store,
sheet_name="V1_Exc_L4").get_segments()[0].get_stored_spike_train_ids()
spike_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L4").get_segments()[0].get_stored_spike_train_ids()
NeuronAnnotationsToPerNeuronValues(data_store, ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Exc_L4')
l4_exc_phase = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentPhase',
sheet_name='V1_Exc_L4')
l4_exc = analog_ids[numpy.argmin([
circular_dist(o, 0, numpy.pi)
for (o, p) in zip(l4_exc_or[0].get_value_by_id(analog_ids),
l4_exc_phase[0].get_value_by_id(analog_ids))
])]
l4_inh_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Inh_L4')
l4_inh_phase = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentPhase',
sheet_name='V1_Inh_L4')
l4_inh = analog_ids_inh[numpy.argmin([
circular_dist(o, 0, numpy.pi)
for (o, p) in zip(l4_inh_or[0].get_value_by_id(analog_ids_inh),
l4_inh_phase[0].get_value_by_id(analog_ids_inh))
])]
GSTA(param_filter_query(data_store, sheet_name='V1_Exc_L4'),
ParameterSet({
'neurons': list(analog_ids),
'length': 250.0
}),
tags=['GSTA']).analyse()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=0,
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': l4_exc,
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview.png').plot()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=0,
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[0],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview1.png').plot()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=0,
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[1],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview2.png').plot()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=0,
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[2],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview3.png').plot()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=0,
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[3],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview4.png').plot()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=0,
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[4],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview5.png').plot()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=0,
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[5],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview6.png').plot()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=0,
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[6],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview7.png').plot()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=0,
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[7],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview8.png').plot()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=0,
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[8],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview9.png').plot()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=0,
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[9],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview10.png').plot()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=0,
st_contrast=100)
KremkowOverviewFigure(dsv,
ParameterSet({
'neuron': analog_ids[10],
'sheet_name': 'V1_Exc_L4'
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name='ExcOverview11.png').plot()