def setup(parameter_set, generate=False):
exec('from parameters_%s import ParameterisedModel'%parameter_set)
params = ParameterisedModel()
cells = ['RIAL', 'RIAR', 'SMDVL', 'SMDVR', 'SMDDL', 'SMDDR', 'AIYL', 'AIYR', 'AWCL', 'AWCR']
#cells = None
cells_to_stimulate = []
reference = "c302_%s_AWC"%parameter_set
target_directory='examples'
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_stimulate=cells_to_stimulate,
duration=800,
dt=0.1,
validate=(parameter_set!='B'),
target_directory = target_directory)
stim_amplitude = "0.6nA"
c302.add_new_input(nml_doc, "AWCL", "100ms", "100ms", stim_amplitude, params)
c302.add_new_input(nml_doc, "AWCR", "500ms", "100ms", stim_amplitude, params)
nml_file = target_directory+'/'+reference+'.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: "+nml_file)
return cells, cells_to_stimulate, params, False
def setup(parameter_set,
generate=False,
duration=500,
dt=0.05,
target_directory='examples',
data_reader="SpreadsheetDataReader",
param_overrides={},
verbose=True):
exec('from parameters_%s import ParameterisedModel' % parameter_set)
params = ParameterisedModel()
stim_amplitudes = ["3pA", "5pA"]
duration = (len(stim_amplitudes)) * 1000
params.set_bioparameter("unphysiological_offset_current_del", "50 ms",
"Testing IClamp", "0")
exc_pre = "URYDL"
exc_post = "SMDDR"
inh_pre = "VD12"
inh_post = "VB11"
gap_1 = "AIZL"
gap_2 = "ASHL"
cells = [exc_pre, exc_post, inh_pre, inh_post]
cells_to_stimulate_extra = [exc_pre, inh_pre]
if parameter_set != 'A':
cells.append(gap_1)
cells.append(gap_2)
cells_to_stimulate_extra.append(gap_1)
reference = "c302_%s_Syns" % parameter_set
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_stimulate=[],
duration=duration,
dt=dt,
target_directory=target_directory,
param_overrides=param_overrides,
verbose=verbose)
for i in range(len(stim_amplitudes)):
start = "%sms" % (i * 1000 + 100)
for c in cells_to_stimulate_extra:
c302.add_new_input(nml_doc, c, start, "800ms", stim_amplitudes[i],
params)
nml_file = target_directory + '/' + reference + '.nml'
writers.NeuroMLWriter.write(
nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate_extra, params, []
def setup(parameter_set,
generate=False,
duration=None,
dt=0.05,
target_directory='examples',
data_reader="SpreadsheetDataReader",
param_overrides={},
config_param_overrides={},
verbose=True):
reference = "c302_%s_IClamp" % parameter_set
c302.print_("Setting up %s" % reference)
exec('from c302.parameters_%s import ParameterisedModel' % parameter_set,
globals())
params = ParameterisedModel()
stim_amplitudes = ["1pA", "2pA", "3pA", "4pA", "5pA", "6pA"]
if duration == None:
duration = (len(stim_amplitudes)) * 1000
my_cells = ["ADAL", "PVCL"]
muscles_to_include = ['MDR01']
cells = my_cells
cells_total = my_cells + muscles_to_include
nml_doc = None
if generate:
c302.print_("Generating %s" % reference)
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_stimulate=[],
muscles_to_include=muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
param_overrides=param_overrides,
verbose=verbose,
data_reader=data_reader)
for i in range(len(stim_amplitudes)):
start = "%sms" % (i * 1000 + 100)
for c in cells_total:
c302.add_new_input(nml_doc, c, start, "800ms",
stim_amplitudes[i], params)
nml_file = target_directory + '/' + reference + '.net.nml'
writers.NeuroMLWriter.write(
nml_doc, nml_file) # Write over network file written above...
c302.print_("(Re)written network file to: " + nml_file)
return cells, cells_total, params, muscles_to_include, nml_doc
def setup(parameter_set,
generate=False,
duration=3000,
dt=0.05,
target_directory='examples',
data_reader="SpreadsheetDataReader",
param_overrides={},
config_param_overrides={},
verbose=True):
exec('from parameters_%s import ParameterisedModel'%parameter_set)
params = ParameterisedModel()
stim_amplitudes = ["1pA","2pA","3pA","4pA","10pA","15pA"]
duration = (len(stim_amplitudes))*1000
cells = ['VB1']
muscles_to_include = ['MVL07']
cells_total = list(cells + muscles_to_include)
reference = "c302_%s_NMJ"%parameter_set
conn_polarity_override = None
if config_param_overrides.has_key('conn_polarity_override'):
conn_polarity_override = config_param_overrides['conn_polarity_override']
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
data_reader=data_reader,
cells=cells,
cells_to_stimulate=[],
muscles_to_include = muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
param_overrides=param_overrides,
conn_polarity_override=conn_polarity_override,
verbose=verbose)
for i in range(len(stim_amplitudes)):
start = "%sms"%(i*1000 + 100)
for c in cells:
c302.add_new_input(nml_doc, c, start, "800ms", stim_amplitudes[i], params)
nml_file = target_directory+'/'+reference+'.net.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: "+nml_file)
return cells, cells_total, params, muscles_to_include, nml_doc
def setup(parameter_set,
generate=False,
duration=3000,
dt=0.05,
target_directory='examples',
data_reader="SpreadsheetDataReader",
param_overrides={},
config_param_overrides={},
verbose=True):
exec('from parameters_%s import ParameterisedModel' % parameter_set)
params = ParameterisedModel()
stim_amplitudes = ["1pA", "2pA", "3pA", "4pA", "10pA", "15pA"]
duration = (len(stim_amplitudes)) * 1000
cells = ['VB1']
muscles_to_include = ['MVL07']
cells_total = list(cells + muscles_to_include)
reference = "c302_%s_NMJ" % parameter_set
conn_polarity_override = None
if config_param_overrides.has_key('conn_polarity_override'):
conn_polarity_override = config_param_overrides[
'conn_polarity_override']
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
data_reader=data_reader,
cells=cells,
cells_to_stimulate=[],
muscles_to_include=muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
param_overrides=param_overrides,
conn_polarity_override=conn_polarity_override,
verbose=verbose)
for i in range(len(stim_amplitudes)):
start = "%sms" % (i * 1000 + 100)
for c in cells:
c302.add_new_input(nml_doc, c, start, "800ms", stim_amplitudes[i],
params)
nml_file = target_directory + '/' + reference + '.nml'
writers.NeuroMLWriter.write(
nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: " + nml_file)
return cells, cells_total, params, muscles_to_include, nml_doc
def setup(parameter_set,
generate=False,
duration=1000,
dt=0.05,
target_directory='examples',
data_reader="SpreadsheetDataReader",
param_overrides={},
verbose=True):
exec('from c302.parameters_%s import ParameterisedModel'%parameter_set, globals())
params = ParameterisedModel()
my_cells = ['RIAL', 'SMDDL','SMDVL']
muscles_to_include = []
cells = my_cells
cells_total = my_cells + muscles_to_include
reference = "c302_%s_RIA"%parameter_set
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_stimulate=muscles_to_include,
muscles_to_include = muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
param_overrides=param_overrides,
verbose=verbose,
data_reader=data_reader)
start1 = '100ms'
dur1 = '100ms'
stim_amplitude1 = '4pA'
start2 = '400ms'
dur2 = '100ms'
stim_amplitude2 = '4pA'
c302.add_new_input(nml_doc, 'SMDDL', start1, dur1, stim_amplitude1, params)
c302.add_new_input(nml_doc, 'SMDVL', start2, dur2, stim_amplitude2, params)
nml_file = target_directory+'/'+reference+'.net.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file) # Write over network file written above...
c302.print_("(Re)written network file to: "+nml_file)
return cells, cells_total, params, muscles_to_include, nml_doc
def setup(parameter_set,
generate=False,
target_directory='examples',
data_reader="SpreadsheetDataReader"):
exec('from parameters_%s import ParameterisedModel' % parameter_set,
globals())
params = ParameterisedModel()
cells = [
'RIAL', 'RIAR', 'SMDBL', 'SMDVR', 'SMDDL', 'SMDDR', 'AIYL', 'AIYR',
'AWCL', 'AWCR'
]
cells_to_stimulate = []
reference = "c302_%s_AWCMike" % parameter_set
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_stimulate=cells_to_stimulate,
duration=800,
dt=0.1,
target_directory=target_directory)
stim_amplitude = "0.6nA" #this is WAY too high to prevent unstable outputs, but fun to watch
c302.add_new_input(nml_doc, "AWCL", "100ms", "100ms", stim_amplitude,
params)
c302.add_new_input(nml_doc, "AWCR", "500ms", "100ms", stim_amplitude,
params)
nml_file = target_directory + '/' + reference + '.net.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file)
print("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate, params, [], nml_doc
def setup(parameter_set,
generate=False,
duration=2500,
dt=0.1,
target_directory='examples'):
exec('from parameters_%s import ParameterisedModel'%parameter_set)
params = ParameterisedModel()
cells = ["RMGR","ASHR","ASKR","AWBR","IL2R","RMHR","URXR"]
cells_to_stimulate = []
reference = "c302_%s_Social"%parameter_set
target_directory='examples'
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_stimulate=cells_to_stimulate,
duration=duration,
dt=dt,
validate=(parameter_set!='B'),
target_directory=target_directory)
stim_amplitude = "5pA"
c302.add_new_input(nml_doc, "RMGR", "100ms", "200ms", stim_amplitude, params)
c302.add_new_input(nml_doc, "ASHR", "400ms", "200ms", stim_amplitude, params)
c302.add_new_input(nml_doc, "ASKR", "700ms", "200ms", stim_amplitude, params)
c302.add_new_input(nml_doc, "AWBR", "1000ms", "200ms", stim_amplitude, params)
c302.add_new_input(nml_doc, "IL2R", "1300ms", "200ms", stim_amplitude, params)
c302.add_new_input(nml_doc, "RMHR", "1600ms", "200ms", stim_amplitude, params)
c302.add_new_input(nml_doc, "URXR", "1900ms", "200ms", stim_amplitude, params)
nml_file = target_directory+'/'+reference+'.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: "+nml_file)
return cells, cells_to_stimulate, params, False
def setup(parameter_set,
generate=False,
duration=2500,
dt=0.1,
target_directory='examples'):
exec('from parameters_%s import ParameterisedModel' % parameter_set)
params = ParameterisedModel()
cells = ["RMGR", "ASHR", "ASKR", "AWBR", "IL2R", "RMHR", "URXR"]
cells_to_stimulate = []
reference = "c302_%s_Social" % parameter_set
target_directory = 'examples'
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_stimulate=cells_to_stimulate,
duration=duration,
dt=dt,
validate=(parameter_set != 'B'),
target_directory=target_directory)
stim_amplitude = "5pA"
c302.add_new_input(nml_doc, "RMGR", "100ms", "200ms", stim_amplitude,
params)
c302.add_new_input(nml_doc, "ASHR", "400ms", "200ms", stim_amplitude,
params)
c302.add_new_input(nml_doc, "ASKR", "700ms", "200ms", stim_amplitude,
params)
c302.add_new_input(nml_doc, "AWBR", "1000ms", "200ms", stim_amplitude,
params)
c302.add_new_input(nml_doc, "IL2R", "1300ms", "200ms", stim_amplitude,
params)
c302.add_new_input(nml_doc, "RMHR", "1600ms", "200ms", stim_amplitude,
params)
c302.add_new_input(nml_doc, "URXR", "1900ms", "200ms", stim_amplitude,
params)
nml_file = target_directory + '/' + reference + '.nml'
writers.NeuroMLWriter.write(
nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate, params, False
def setup(
parameter_set,
generate=False,
duration=10000, # Change here the duration of the simulation in ms
dt=0.05, # Time step
target_directory='examples',
data_reader="UpdatedSpreadsheetDataReader2",
param_overrides={},
verbose=True,
config_param_overrides={}):
# Import this from the defined parameters.
exec('from parameters_%s import ParameterisedModel' % parameter_set,
globals())
params = ParameterisedModel()
#*******************************************
# 1. DEFINE THE ELEMENTS TO SIMULATE
#*******************************************
cells = []
# Neurons to include in the simulation (e.g. cells = ['AVBL', 'DB1'])
muscles_to_include = []
# Muscles to include in the simulation (e.g. muscles_to_include = ['MDL07'], muscles = True for all the muscles)
cells_to_stimulate = []
# Neurons to stimulate
# This is used to stimulate the neurons and muscles, I use a different method to stimulate however.
cells_to_plot = cells
# Neurons to include in the plot (e.g. cells_to_plot = ['DB1'])
# If you do not want to plot all the neurons defined, change cells by the only neurons you want plotted.
reference = "c302_%s_tutorial" % parameter_set
# Reference for the subsequent generated NeuroML files
# (Make sure this corresponds with the script name: c302_NAME.py --> reference = "c302_%s_NAME" % parameter_set)
#*******************************************
# 2. DEFINE THE NETWORK CONNECTIONS
#*******************************************
conns_to_include = []
# Only connections to include
# By default, c302 takes all the connections from the connectome.
# If you only want some specific connections, specify them here.
conns_to_exclude = [
'DB1-DB2', # Exclude the chemical synapse from DB1 to DB2
'DB1-DB2_GJ', # Exclude the gap junction from DB1 to DB2
'DB2-DB1_GJ',
# Note that gap junctions need to be excluded in both ways
r'^AVB.-DB\d+$', # Regular expression to exclude AVBR and AVBL synapses to DB1, DB2... DB7
]
# Connections to exclude or 'ablate'
# I included some examples of connections to be excluded.
# Search on how to use Regular Expressions in python.
conn_polarity_override = {
r'^VB\d+-VB\d+$': 'inh', #Change any VB# to VB# synapse to inhibitory
r'^DD\d+-DD\d+$': 'exc', #Change any DD# to DD# synapse to excitatory
}
# Change the polarity of chemical synapses
# c302 takes as inhibitory those synapses coming from GABAergic neurons.
conn_number_override = {
'^.+-.+$': 1,
}
# Changed the number of synapses between neurons all to 1.
#*******************************************
# 3. ADD INPUTS TO NEURONS AND MUSCLES
#*******************************************
# (This is where I define the inputs, instead of using cells_to_stimulate)
input_list = [] # Step current input
sine_input_list = [] # Sinusoidal input
ramp_input_list = [] # Ramp inputs are still under development
input_list.append((neuron, start, duration, amplitude))
# Add a step input to a neuron, at a given time, with a duration and intensity.
# This can also be used to stimulate muscles!
# (e.g. input_list.append(('AVBL', '0ms', '4000ms', '15pA')))
sine_input_list.append((neuron, start, duration, peak_amplitude, period))
# Add a step input to a neuron, at a given time, with a duration and intensity.
# This can also be used to stimulate muscles!
# (e.g. sine_input_list.append(('DB1', '0ms', '5000ms', '1.5pA', '800ms')) )
config_param_overrides['input'] = input_list
#*******************************************
# 4. OVERRIDE CERTAIN PARAMETERS
#*******************************************
param_overrides = {
# Symmetrical Gap junctions
'mirrored_elec_conn_params': {
#r'^AVB._to_AVB._GJ$_elec_syn_gbase': '0.001 nS',
},
# Change the initial membrane potential
'initial_memb_pot': '-50 mV',
# Here, any parameters found in the Parameters_#.py can be changed and specified for this specific simulation.
}
nml_doc = None
# Create the NeuroML file of the network using c302 functions.
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_plot=cells_to_plot,
cells_to_stimulate=cells_to_stimulate,
conns_to_include=conns_to_include,
conns_to_exclude=conns_to_exclude,
conn_polarity_override=conn_polarity_override,
conn_number_override=conn_number_override,
muscles_to_include=muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
data_reader=data_reader,
param_overrides=param_overrides,
verbose=verbose)
for stim_input in input_list:
cell, start, dur, current = stim_input
c302.add_new_input(nml_doc, cell, start, dur, current, params)
for sine_stim_input in sine_input_list:
cell, delay, dur, amp, period = sine_stim_input
c302.add_new_sinusoidal_input(nml_doc, cell, delay, dur, amp,
period, params)
for ramp_stim_input in ramp_input_list:
cell, delay, dur, start_amp, finish_amp, base_amp = ramp_stim_input
c302.add_new_ramp_input(nml_doc, cell, delay, dur, start_amp,
finish_amp, base_amp, params)
nml_file = target_directory + '/' + reference + '.net.nml'
writers.NeuroMLWriter.write(
nml_doc, nml_file) # Write over network file written above...
c302.print_("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate, params, muscles_to_include, nml_doc
def setup(parameter_set,
generate=False,
duration=6000,
dt=0.05,
target_directory='examples',
data_reader="UpdatedSpreadsheetDataReader2",
param_overrides={},
verbose=True,
config_param_overrides={}):
exec ('from parameters_%s import ParameterisedModel' % parameter_set, globals())
params = ParameterisedModel()
#'''
VA_motors = ["VA%s" % c for c in range_incl(1, 12)]
VB_motors = ["VB%s" % c for c in range_incl(1, 11)]
DA_motors = ["DA%s" % c for c in range_incl(1, 9)]
DB_motors = ["DB%s" % c for c in range_incl(1, 7)]
DD_motors = ["DD%s" % c for c in range_incl(1, 6)]
VD_motors = ["VD%s" % c for c in range_incl(1, 13)]
AS_motors = ["AS%s" % c for c in range_incl(1, 11)]
#'''
motors = list(VA_motors + VB_motors + AS_motors + DA_motors + DB_motors + VD_motors + DD_motors)
inters = ['AVBL', 'AVBR', 'AVAL', 'AVAR']
cells = list(motors + inters)
muscles_to_include = []
cells_to_stimulate = []
cells_to_plot = cells
reference = "c302_%s_BW" % parameter_set
conns_to_include = [
]
conns_to_exclude = [
'VB2-VB4_GJ',
'VB4-VB2_GJ',
#########################################
# Remove unwanted interneuron connections
#########################################
# Disconnect the AVA and AVB interneurons
r'^AVB.-AVA.$',
r'^AVA.-AVB.$',
r'^AVB.-AVA._GJ$',
r'^AVA.-AVB._GJ$',
# Disconnect chemical stimulation from AVA and AVB
r'^AVB.-.A\d+$',
r'^AVB.-.B\d+$',
r'^AVB.-.D\d+$',
r'^AVB.-AS\d+$',
r'^AVA.-.A\d+$',
r'^AVA.-.B\d+$',
r'^AVA.-.D\d+$',
r'^AVA.-AS\d+$',
# Disconnect AVA and AVB gap junctions not pressent.
r'^AVB.-.A\d+_GJ$',
r'^AVB.-AS\d+_GJ$',
r'^AVB.-.D\d+_GJ$',
r'^AVA.-.B\d+_GJ$',
r'^AVA.-AS\d+_GJ$',
r'^AVA.-.D\d+_GJ$',
# Disconnect feedback GJ into AVA and AVB.
r'^..\d+-AV.._GJ$',
#########################################################
# Remove connections not present in Haspel and O'Donovan
#########################################################
#'''
r'^AS\d+-.B\d+$',
r'^AS\d+-VA\d+$',
r'^AS\d+-DD\d+$',
r'^AS\d+-..\d+_GJ$',
r'^..\d+-AS\d+_GJ$',
r'^DA\d+-AS\d+$',
r'^DA\d+-DD\d+$',
r'^DA\d+-VB\d+$',
r'^DA\d+-VA\d+$',
r'^DA\d+-AS\d+$',
r'^DA\d+-.B\d+_GJ$',
r'^DA\d+-.D\d+_GJ$',
r'^.B\d+-DA\d+_GJ$',
r'^.D\d+-DA\d+_GJ$',
r'^DB\d+-.A\d+$',
r'^DB\d+-VB\d+$',
r'^DB\d+-.A\d+_GJ$',
r'^DB\d+-.D\d+_GJ$',
r'^DB\d+-VB\d+_GJ$',
r'^.A\d+-DB\d+_GJ$',
r'^.D\d+-DB\d+_GJ$',
r'^VB\d+-DB\d+_GJ$',
r'^DD\d+-..\d+$',
r'^DD\d+-VD\d+_GJ$',
r'^DD\d+-.B\d+_GJ$',
r'^DD\d+-.A\d+_GJ$',
r'^VD\d+-DD\d+_GJ$',
r'^.B\d+-DD\d+_GJ$',
r'^.A\d+-DD\d+_GJ$',
r'^VD\d+-D.\d+$',
r'^VD\d+-AS\d+$',
r'^VD\d+-VB\d+_GJ$',
r'^VB\d+-VD\d+_GJ$',
r'^VB\d+-DB\d+$',
r'^VB\d+-.A\d+$',
r'^VB\d+-AS\d+$',
r'^VB\d+-VD\d+$',
r'^VB\d+-VA\d+_GJ$',
r'^VA\d+-VB\d+_GJ$',
r'^VA\d+-.B\d+$',
r'^VA\d+-DA\d+$',
r'^VA\d+-AS\d+$',
###############################################
# Remove connections going forward in DA and VA
###############################################
#Forward connections in DA-VA
'DA3-DA4',
'DA2-DA3',
'VA2-VA3',
'VA3-VA4',
'VA5-VA6',
'DA9-VA12',
'VA12-DA8',
'VA12-DA9',
'VA1-DA2',
#'''
]
conn_polarity_override = {
#Inhibitory in Olivares
r'^AS\d+-VD\d+$': 'inh',
r'^DB\d+-AS\d+$': 'inh',
r'^DB\d+-VD\d+$': 'inh',
r'^VD\d+-VA\d+$': 'inh',
r'^VA\d+-VD\d+$': 'inh',
#Excitatory in Olivares
r'^VD\d+-VB\d+$': 'exc',
#Inhibitory in LUNG
#r'^DB\d+-DD\d+$': 'inh',
#r'^VB\d+-VD\d+$': 'inh',
#Inhibitory for DD
r'^DA\d+-DD\d+$': 'inh',
r'^AS\d+-DD\d+$': 'inh',
r'^DB\d+-VD\d+$': 'inh',
}
conn_polarity_override = {
#### NEW CIRCUIT ####
r'^VA\d+-VD\d+$': 'inh',
r'^DA\d+-DD\d+$': 'inh',
r'^AS\d+-DD\d+$': 'inh',
r'^DB\d+-DD\d+$': 'inh',
r'^AS\d+-VD\d+$': 'inh',
#VD-DD and VD-VB are already inhibitory
}
conn_number_override = {
'^.+-.+$': 1,
}
#*********
# INPUTS
#*********
input_list = []
sine_input_list = []
ramp_input_list = []
#*************************
# Interneuron STIMULATION
#*************************
#'''
input_list.append(('AVAL', '0ms', '4000ms', '15pA'))
input_list.append(('AVAR', '0ms', '4000ms', '15pA'))
#'''
#*************************
# DB1 and VB1 STIMULATION
#*************************
# Sinusoidal Input
#'''
#sine_input_list.append(('DB1', '0ms', '15000ms', '1.5pA', '800ms')) #AMP: 2pA seems to overstimulate
#sine_input_list.append(('VB1', '0ms', '15000ms', '1.5pA', '800ms'))
sine_input_list.append(('DA9', '0ms', '15000ms', '1.5pA', '800ms'))
sine_input_list.append(('VA12', '0ms', '15000ms', '-1.5pA', '800ms'))
config_param_overrides['input'] = input_list
param_overrides = {
'mirrored_elec_conn_params': {
#Connections to DB1 and VB1 have a HIGHER conductance
r'^AVB._to_.B1_GJ$_elec_syn_gbase': '0.005 nS',
#Connections to rest of DB and VB have a LOWER conductance
r'^AVB._to_.B[2-9]\_GJ$_elec_syn_gbase': '0.001 nS',
r'^AVB._to_.B[1-9][0-9]\_GJ$_elec_syn_gbase': '0.001 nS',
#Connections to DA9 and VA12 have a HIGHER conductance
r'^AVA._to_DA9_GJ$_elec_syn_gbase': '0.005 nS',
r'^AVA._to_VA12_GJ$_elec_syn_gbase': '0.005 nS',
#Connections to rest of DA and VA have LOWER conductance
r'^AVA._to_DA[1-8]\_GJ$_elec_syn_gbase': '0.001 nS',
r'^AVA._to_VA[1-9][0-1]?\_GJ$_elec_syn_gbase': '0.001 nS',
r'^.B\d+_to_.B\d+\_GJ$_elec_syn_gbase': '0.001 nS',
r'^.A\d+_to_.A\d+\_GJ$_elec_syn_gbase': '0.001 nS',
r'^.D\d+_to_.D\d+\_GJ$_elec_syn_gbase': '0.001 nS',
r'^AS\d+_to_AS\d+\_GJ$_elec_syn_gbase': '0.001 nS',
r'^VA\d+_to_DA\d+\_GJ$_elec_syn_gbase': '0.001 nS',
r'^VA\d+_to_VD\d+\_GJ$_elec_syn_gbase': '0.001 nS',
},
'initial_memb_pot': '-50 mV',
##### Adjustments ######
r'^DA\d+_to_DB\d+$_exc_syn_conductance': '0.2 nS',
r'^DB\d+_to_VD\d+$_exc_syn_conductance': '0.2 nS',
#*********************************
# Connections between units (chemical)
#*********************************
#r'^VD\d+_to_VB\d+$_inh_syn_conductance': '0.2 nS',
#r'^DA\d+_to_VD\d+$_exc_syn_conductance': '0.2 nS',
#Connect synaptically VB1 to VB2 and so on
r'^VB\d+_to_VB\d+$_exc_syn_conductance': '30 nS',
r'^VB\d+_to_VB\d+$_exc_syn_ar': '0.19 per_s',
r'^VB\d+_to_VB\d+$_exc_syn_ad': '73 per_s',
r'^VB\d+_to_VB\d+$_exc_syn_beta': '2.81 per_mV',
r'^VB\d+_to_VB\d+$_exc_syn_vth': '-22 mV',
r'^VB\d+_to_VB\d+$_exc_syn_erev': '10 mV',
#Connect synaptically DB1 to DB2 and so on
r'^DB\d+_to_DB\d+$_exc_syn_conductance': '30 nS',
r'^DB\d+_to_DB\d+$_exc_syn_ar': '0.08 per_s',
r'^DB\d+_to_DB\d+$_exc_syn_ad': '18 per_s',
r'^DB\d+_to_DB\d+$_exc_syn_beta': '0.21 per_mV',
r'^DB\d+_to_DB\d+$_exc_syn_vth': '-10 mV',
r'^DB\d+_to_DB\d+$_exc_syn_erev': '10 mV',
#'''
#Connect synaptically VA1 to VA2 and so on
r'^VA\d+_to_VA\d+$_exc_syn_conductance': '30 nS',
r'^VA\d+_to_VA\d+$_exc_syn_ar': '0.19 per_s',
r'^VA\d+_to_VA\d+$_exc_syn_ad': '73 per_s',
r'^VA\d+_to_VA\d+$_exc_syn_beta': '2.81 per_mV',
r'^VA\d+_to_VA\d+$_exc_syn_vth': '-22 mV',
r'^VA\d+_to_VA\d+$_exc_syn_erev': '10 mV',
#Connect synaptically DB1 to DB2 and so on
r'^DA\d+_to_DA\d+$_exc_syn_conductance': '30 nS',
r'^DA\d+_to_DA\d+$_exc_syn_ar': '0.08 per_s',
r'^DA\d+_to_DA\d+$_exc_syn_ad': '18 per_s',
r'^DA\d+_to_DA\d+$_exc_syn_beta': '0.21 per_mV',
r'^DA\d+_to_DA\d+$_exc_syn_vth': '-10 mV',
r'^DA\d+_to_DA\d+$_exc_syn_erev': '10 mV',
#'''
'AVBR_to_MVL16_exc_syn_conductance': '0 nS',
'ca_conc_decay_time_muscle': '60.8 ms',
'ca_conc_rho_muscle': '0.002338919 mol_per_m_per_A_per_s',
}
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_plot=cells_to_plot,
cells_to_stimulate=cells_to_stimulate,
conns_to_include=conns_to_include,
conns_to_exclude=conns_to_exclude,
conn_polarity_override=conn_polarity_override,
conn_number_override=conn_number_override,
muscles_to_include=muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
data_reader=data_reader,
param_overrides=param_overrides,
verbose=verbose)
#if config_param_overrides.has_key('input'):
# input_list = config_param_overrides['input']
for stim_input in input_list:
cell, start, dur, current = stim_input
c302.add_new_input(nml_doc, cell, start, dur, current, params)
for sine_stim_input in sine_input_list:
cell, delay, dur, amp, period = sine_stim_input
c302.add_new_sinusoidal_input(nml_doc, cell, delay, dur, amp, period, params)
for ramp_stim_input in ramp_input_list:
cell, delay, dur, start_amp, finish_amp, base_amp = ramp_stim_input
c302.add_new_ramp_input(nml_doc, cell, delay, dur, start_amp, finish_amp, base_amp, params)
#c302.add_new_input(nml_doc, cell='VB1', delay="1200ms", duration="1000ms", amplitude="1pA", params=params)
#c302.add_new_input(nml_doc, cell='VB1', delay="0ms", duration="2000ms", amplitude="1.5pA", params=params)
nml_file = target_directory + '/' + reference + '.net.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file) # Write over network file written above...
c302.print_("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate, params, muscles_to_include, nml_doc
def setup(parameter_set,
generate=False,
duration=2000,
dt=0.05,
target_directory='../../../examples',
data_reader="SpreadsheetDataReader",
param_overrides={},
verbose=True,
config_param_overrides={}):
exec ('from parameters_%s import ParameterisedModel' % parameter_set)
params = ParameterisedModel()
params.set_bioparameter("unphysiological_offset_current", "0pA", "Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_del", "0 ms", "Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_dur", "2000 ms", "Testing TapWithdrawal", "0")
VA_motors = ["VA%s" % c for c in range_incl(1, 12)]
VB_motors = ["VB%s" % c for c in range_incl(1, 11)]
DA_motors = ["DA%s" % c for c in range_incl(1, 9)]
DB_motors = ["DB%s" % c for c in range_incl(1, 7)]
DD_motors = ["DD%s" % c for c in range_incl(1, 6)]
VD_motors = ["VD%s" % c for c in range_incl(1, 13)]
AS_motors = ["AS%s" % c for c in range_incl(1, 11)]
cells = list(['AVAL', 'AVAR'] + VA_motors + DA_motors + VD_motors + DD_motors)
muscles_to_include = True
cells_to_stimulate = []
cells_to_plot = list(cells)
reference = "c302_%s_AVA_VA_DA_VD_DD" % parameter_set
conns_to_include = [
]
conn_polarity_override = {}
if config_param_overrides.has_key('conn_polarity_override'):
conn_polarity_override.update(config_param_overrides['conn_polarity_override'])
conn_number_override = {}
if config_param_overrides.has_key('conn_number_override'):
conn_number_override.update(config_param_overrides['conn_number_override'])
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_plot=cells_to_plot,
cells_to_stimulate=cells_to_stimulate,
conns_to_include=conns_to_include,
conn_polarity_override=conn_polarity_override,
conn_number_override=conn_number_override,
muscles_to_include=muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
data_reader=data_reader,
param_overrides=param_overrides,
verbose=verbose)
end = int(duration) - 500
#for vb in VB_motors:
# c302.add_new_sinusoidal_input(nml_doc, cell=vb, delay="0ms", duration="1000ms", amplitude="3pA",
# period="700ms", params=params)
#for db in DB_motors:
# c302.add_new_sinusoidal_input(nml_doc, cell=db, delay="0ms", duration="1000ms", amplitude="3pA",
# period="700ms", params=params)
c302.add_new_input(nml_doc, "AVAL", "50ms", "%sms"%end, "15pA", params)
c302.add_new_input(nml_doc, "AVAR", "50ms", "%sms"%end, "15pA", params)
nml_file = target_directory + '/' + reference + '.net.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate, params, muscles_to_include, nml_doc
def setup(parameter_set,
generate=False,
duration=2500,
dt=0.05,
target_directory='examples',
data_reader="SpreadsheetDataReader",
param_overrides={},
config_param_overrides={},
verbose=True):
exec('from parameters_%s import ParameterisedModel'%parameter_set, globals())
params = ParameterisedModel()
cells = ["RMGR","ASHR","ASKR","AWBR","IL2R","RMHR","URXR"]
cells_to_stimulate = []
reference = "c302_%s_Social"%parameter_set
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_stimulate=cells_to_stimulate,
duration=duration,
dt=dt,
target_directory=target_directory,
param_overrides=param_overrides,
verbose=verbose,
data_reader=data_reader)
stim_amplitude = "5pA"
c302.add_new_input(nml_doc, "RMGR", "100ms", "200ms", stim_amplitude, params)
c302.add_new_input(nml_doc, "ASHR", "400ms", "200ms", stim_amplitude, params)
c302.add_new_input(nml_doc, "ASKR", "700ms", "200ms", stim_amplitude, params)
c302.add_new_input(nml_doc, "AWBR", "1000ms", "200ms", stim_amplitude, params)
c302.add_new_input(nml_doc, "IL2R", "1300ms", "200ms", stim_amplitude, params)
c302.add_new_input(nml_doc, "RMHR", "1600ms", "200ms", stim_amplitude, params)
c302.add_new_input(nml_doc, "URXR", "1900ms", "200ms", stim_amplitude, params)
nml_file = target_directory+'/'+reference+'.net.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: "+nml_file)
return cells, cells_to_stimulate, params, [], nml_doc
def setup(parameter_set,
generate=False,
duration=2000,
dt=0.05,
target_directory='examples',
data_reader="SpreadsheetDataReader",
param_overrides={},
verbose=True,
config_param_overrides={}):
exec('from parameters_%s import ParameterisedModel' % parameter_set,
globals())
params = ParameterisedModel()
params.set_bioparameter("unphysiological_offset_current", "0pA",
"Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_del", "0 ms",
"Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_dur", "2000 ms",
"Testing TapWithdrawal", "0")
VA_motors = ["VA%s" % c for c in range_incl(1, 12)]
VB_motors = ["VB%s" % c for c in range_incl(1, 11)]
DA_motors = ["DA%s" % c for c in range_incl(1, 9)]
DB_motors = ["DB%s" % c for c in range_incl(1, 7)]
DD_motors = ["DD%s" % c for c in range_incl(1, 6)]
VD_motors = ["VD%s" % c for c in range_incl(1, 13)]
AS_motors = ["AS%s" % c for c in range_incl(1, 11)]
cells = list(['AVBL', 'AVBR'] + DB_motors + VD_motors + VB_motors +
DD_motors)
muscles_to_include = True
cells_to_stimulate = []
cells_to_plot = list(cells)
reference = "c302_%s_FW" % parameter_set
conns_to_include = []
conns_to_exclude = [
'VB2-VB4_GJ',
'VB4-VB2_GJ',
]
conn_polarity_override = {
r'^DB\d+-DD\d+$': 'inh',
r'^VB\d+-VD\d+$': 'inh',
}
conn_number_override = {
'^.+-.+$': 1,
}
input_list = []
'''dur = '250ms'
amp = '3pA'
for muscle_num in range(24):
mdlx = 'MDL0%s' % (muscle_num + 1)
mdrx = 'MDR0%s' % (muscle_num + 1)
#mvlx = 'MVL0%s' % (muscle_num + 1)
#mvrx = 'MVR0%s' % (muscle_num + 1)
if muscle_num >= 9:
mdlx = 'MDL%s' % (muscle_num + 1)
mdrx = 'MDR%s' % (muscle_num + 1)
#mvlx = 'MVL%s' % (muscle_num + 1)
#mvrx = 'MVR%s' % (muscle_num + 1)
startd = '%sms' % (muscle_num * 10)
#startv = '%sms' % ((stim_num * 800 + 400) + muscle_num * 30)
input_list.append((mdlx, startd, dur, amp))
input_list.append((mdrx, startd, dur, amp))'''
input_list.append(('MVR10', '0ms', '150ms', '1pA'))
input_list.append(('MVR11', '0ms', '150ms', '2pA'))
input_list.append(('MVR12', '0ms', '150ms', '3pA'))
input_list.append(('MVR13', '0ms', '150ms', '3pA'))
input_list.append(('MVR14', '0ms', '150ms', '2pA'))
input_list.append(('MVR15', '0ms', '150ms', '1pA'))
input_list.append(('MVL10', '0ms', '150ms', '1pA'))
input_list.append(('MVL11', '0ms', '150ms', '2pA'))
input_list.append(('MVL12', '0ms', '150ms', '3pA'))
input_list.append(('MVL13', '0ms', '150ms', '3pA'))
input_list.append(('MVL14', '0ms', '150ms', '2pA'))
input_list.append(('MVL15', '0ms', '150ms', '1pA'))
input_list.append(('MDL21', '0ms', '250ms', '3pA'))
input_list.append(('MDL22', '0ms', '250ms', '3pA'))
input_list.append(('MDR21', '0ms', '250ms', '3pA'))
input_list.append(('MDR22', '0ms', '250ms', '3pA'))
amp = '4pA'
dur = '250ms'
for stim_num in range(15):
for muscle_num in range(7):
mdlx = 'MDL0%s' % (muscle_num + 1)
mdrx = 'MDR0%s' % (muscle_num + 1)
mvlx = 'MVL0%s' % (muscle_num + 1)
mvrx = 'MVR0%s' % (muscle_num + 1)
if muscle_num >= 9:
mdlx = 'MDL%s' % (muscle_num + 1)
mdrx = 'MDR%s' % (muscle_num + 1)
mvlx = 'MVL%s' % (muscle_num + 1)
mvrx = 'MVR%s' % (muscle_num + 1)
startd = '%sms' % (stim_num * 800 + muscle_num * 30)
startv = '%sms' % ((stim_num * 800 + 400) + muscle_num * 30)
input_list.append((mdlx, startd, dur, amp))
input_list.append((mdrx, startd, dur, amp))
if muscle_num != 6:
input_list.append((mvlx, startv, dur, amp))
input_list.append((mvrx, startv, dur, amp))
d_v_delay = 400
start = 190
motor_dur = '250ms'
input_list.append(('AVBL', '0ms', '%sms' % duration, '15pA'))
input_list.append(('AVBR', '0ms', '%sms' % duration, '15pA'))
input_list.append(('DB1', '%sms' % (start), motor_dur, '3pA'))
input_list.append(('VB1', '%sms' % (start + d_v_delay), motor_dur, '3pA'))
i = start + 2 * d_v_delay
j = start + 3 * d_v_delay
for pulse_num in range(1, 15):
input_list.append(('DB1', '%sms' % i, motor_dur, '3pA'))
input_list.append(('VB1', '%sms' % j, motor_dur, '3pA'))
i += d_v_delay * 2
j += d_v_delay * 2
#input_list = []
#input_list.append(('AVBL', '0ms', '1900ms', '15pA'))
#input_list.append(('AVBR', '0ms', '1900ms', '15pA'))
config_param_overrides['input'] = input_list
param_overrides = {
'mirrored_elec_conn_params': {
r'^AVB._to_DB\d+\_GJ$_elec_syn_gbase': '0.001 nS',
r'^AVB._to_VB\d+\_GJ$_elec_syn_gbase': '0.001 nS',
r'^DB\d+_to_DB\d+\_GJ$_elec_syn_gbase': '0.001 nS',
#'^DB\d+_to_DB\d+\_GJ$_elec_syn_p_gbase': '0.08 nS',
#'^DB\d+_to_DB\d+\_GJ$_elec_syn_sigma': '0.2 per_mV',
#'^DB\d+_to_DB\d+\_GJ$_elec_syn_mu': '-20 mV',
r'^VB\d+_to_VB\d+\_GJ$_elec_syn_gbase': '0.001 nS',
#'^VB\d+_to_VB\d+\_GJ$_elec_syn_p_gbase': '0.1 nS',
#'^VB\d+_to_VB\d+\_GJ$_elec_syn_sigma': '0.3 per_mV',
#'^VB\d+_to_VB\d+\_GJ$_elec_syn_mu': '-30 mV',
#'VB2_to_VB4_elec_syn_gbase': '0 nS',
r'^DB\d+_to_VB\d+\_GJ$_elec_syn_gbase': '0 nS',
r'^DB\d+_to_DD\d+\_GJ$_elec_syn_gbase': '0 nS',
r'^VB\d+_to_VD\d+\_GJ$_elec_syn_gbase': '0 nS',
#'^VD\d+_to_DD\d+\_GJ$_elec_syn_gbase': '0 nS',
'DD1_to_MVL08_elec_syn_gbase': '0 nS',
'VD2_to_MDL09_elec_syn_gbase': '0 nS',
},
r'^VB\d+_to_VB\d+$_exc_syn_conductance': '18 nS',
r'^VB\d+_to_VB\d+$_exc_syn_ar': '0.19 per_s',
r'^VB\d+_to_VB\d+$_exc_syn_ad': '73 per_s',
r'^VB\d+_to_VB\d+$_exc_syn_beta': '2.81 per_mV',
r'^VB\d+_to_VB\d+$_exc_syn_vth': '-22 mV',
r'^VB\d+_to_VB\d+$_exc_syn_erev': '10 mV',
r'^DB\d+_to_DB\d+$_exc_syn_conductance': '20 nS',
r'^DB\d+_to_DB\d+$_exc_syn_ar': '0.08 per_s',
r'^DB\d+_to_DB\d+$_exc_syn_ad': '18 per_s',
r'^DB\d+_to_DB\d+$_exc_syn_beta': '0.21 per_mV',
r'^DB\d+_to_DB\d+$_exc_syn_vth': '-10 mV',
r'^DB\d+_to_DB\d+$_exc_syn_erev': '10 mV',
'initial_memb_pot': '-50 mV',
'AVBR_to_DB4_exc_syn_conductance': '0 nS',
#'VB4_to_VB5_exc_syn_conductance': '0 nS',
'AVBL_to_VB2_exc_syn_conductance': '0 nS',
'AVBR_to_VD3_exc_syn_conductance': '0 nS',
#'^DB\d+_to_DD\d+$_exc_syn_conductance': '0 nS',
#'^DD\d+_to_DB\d+$_inh_syn_conductance': '0 nS',
#'^VB\d+_to_VD\d+$_exc_syn_conductance': '0 nS',
#'^VD\d+_to_VB\d+$_inh_syn_conductance': '0 nS',
'DD1_to_VB2_inh_syn_conductance': '0 nS',
'neuron_to_muscle_exc_syn_conductance': '0.5 nS',
r'^DB\d+_to_MDL\d+$_exc_syn_conductance': '0.4 nS',
r'^DB\d+_to_MDR\d+$_exc_syn_conductance': '0.4 nS',
r'^VB\d+_to_MVL\d+$_exc_syn_conductance': '0.6 nS',
r'^VB\d+_to_MVR\d+$_exc_syn_conductance': '0.6 nS',
'neuron_to_muscle_exc_syn_vth': '37 mV',
'neuron_to_muscle_inh_syn_conductance': '0.6 nS',
'neuron_to_neuron_inh_syn_conductance': '0.2 nS',
#'DB2_to_MDL11_exc_syn_conductance': '1 nS',
'AVBR_to_MVL16_exc_syn_conductance': '0 nS',
'ca_conc_decay_time_muscle': '60.8 ms',
'ca_conc_rho_muscle': '0.002338919 mol_per_m_per_A_per_s',
}
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_plot=cells_to_plot,
cells_to_stimulate=cells_to_stimulate,
conns_to_include=conns_to_include,
conns_to_exclude=conns_to_exclude,
conn_polarity_override=conn_polarity_override,
conn_number_override=conn_number_override,
muscles_to_include=muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
data_reader=data_reader,
param_overrides=param_overrides,
verbose=verbose)
#if config_param_overrides.has_key('input'):
# input_list = config_param_overrides['input']
for stim_input in input_list:
cell, start, dur, current = stim_input
c302.add_new_input(nml_doc, cell, start, dur, current, params)
nml_file = target_directory + '/' + reference + '.net.nml'
writers.NeuroMLWriter.write(
nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate, params, muscles_to_include, nml_doc
def setup(parameter_set,
generate=False,
duration=2000,
dt=0.05,
target_directory='examples',
data_reader="SpreadsheetDataReader",
param_overrides={},
verbose=True,
config_param_overrides={}):
exec('from parameters_%s import ParameterisedModel'%parameter_set, globals())
params = ParameterisedModel()
params.set_bioparameter("unphysiological_offset_current", "0pA", "Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_del", "0 ms", "Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_dur", "2000 ms", "Testing TapWithdrawal", "0")
VA_motors = ["VA%s" % c for c in range_incl(1, 12)]
VB_motors = ["VB%s" % c for c in range_incl(1, 11)]
DA_motors = ["DA%s" % c for c in range_incl(1, 9)]
DB_motors = ["DB%s" % c for c in range_incl(1, 7)]
DD_motors = ["DD%s" % c for c in range_incl(1, 6)]
VD_motors = ["VD%s" % c for c in range_incl(1, 13)]
AS_motors = ["AS%s" % c for c in range_incl(1, 11)]
cells = []
muscles_to_include = True
if config_param_overrides.has_key('muscles_to_include'):
muscles_to_include = config_param_overrides['muscles_to_include']
cells_to_stimulate = []
cells_to_plot = list(cells)
reference = "c302_%s_MuscleTest" % parameter_set
conns_to_include = []
if config_param_overrides.has_key('conns_to_include'):
conns_to_include = config_param_overrides['conns_to_include']
conns_to_exclude = ['^.+-.+$']
if config_param_overrides.has_key('conns_to_exclude'):
conns_to_exclude = config_param_overrides['conns_to_exclude']
conn_polarity_override = {}
if config_param_overrides.has_key('conn_polarity_override'):
conn_polarity_override.update(config_param_overrides['conn_polarity_override'])
conn_number_override = {
}
if config_param_overrides.has_key('conn_number_override'):
conn_number_override.update(config_param_overrides['conn_number_override'])
param_overrides = {
'ca_conc_decay_time_muscle': '60 ms',
'ca_conc_rho_muscle': '0.002138919 mol_per_m_per_A_per_s',
}
end = '%sms' % (int(duration) - 100)
input_list = []
#input_list.append(('MDL02', '0ms', '250ms', '3pA'))
#input_list.append(('MDL03', '0ms', '250ms', '3pA'))
#input_list.append(('MDR02', '0ms', '250ms', '3pA'))
#input_list.append(('MDR03', '0ms', '250ms', '3pA'))
input_list.append(('MVR10', '0ms', '250ms', '1pA'))
input_list.append(('MVR11', '0ms', '250ms', '2pA'))
input_list.append(('MVR12', '0ms', '250ms', '3pA'))
input_list.append(('MVR13', '0ms', '250ms', '3pA'))
input_list.append(('MVR14', '0ms', '250ms', '2pA'))
input_list.append(('MVR15', '0ms', '250ms', '1pA'))
input_list.append(('MVL10', '0ms', '250ms', '1pA'))
input_list.append(('MVL11', '0ms', '250ms', '2pA'))
input_list.append(('MVL12', '0ms', '250ms', '3pA'))
input_list.append(('MVL13', '0ms', '250ms', '3pA'))
input_list.append(('MVL14', '0ms', '250ms', '2pA'))
input_list.append(('MVL15', '0ms', '250ms', '1pA'))
input_list.append(('MDL21', '0ms', '250ms', '3pA'))
input_list.append(('MDL22', '0ms', '250ms', '3pA'))
input_list.append(('MDR21', '0ms', '250ms', '3pA'))
input_list.append(('MDR22', '0ms', '250ms', '3pA'))
for stim_num in range(5):
for muscle_num in range(24):
mdlx = 'MDL0%s' % (muscle_num + 1)
mdrx = 'MDR0%s' % (muscle_num + 1)
mvlx = 'MVL0%s' % (muscle_num + 1)
mvrx = 'MVR0%s' % (muscle_num + 1)
if muscle_num >= 9:
mdlx = 'MDL%s' % (muscle_num + 1)
mdrx = 'MDR%s' % (muscle_num + 1)
mvlx = 'MVL%s' % (muscle_num + 1)
if muscle_num != 23:
mvrx = 'MVR%s' % (muscle_num + 1)
startd = '%sms' % (stim_num * 1000 + muscle_num * 50)
startv = '%sms' % ((stim_num * 1000 + 500) + muscle_num * 50)
dur = '250ms'
amp = '3pA'
input_list.append((mdlx, startd, dur, amp))
input_list.append((mdrx, startd, dur, amp))
input_list.append((mvlx, startv, dur, amp))
input_list.append((mvrx, startv, dur, amp))
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_plot=cells_to_plot,
cells_to_stimulate=cells_to_stimulate,
conns_to_include=conns_to_include,
conns_to_exclude=conns_to_exclude,
conn_polarity_override=conn_polarity_override,
conn_number_override=conn_number_override,
muscles_to_include=muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
data_reader=data_reader,
param_overrides=param_overrides,
verbose=verbose)
if config_param_overrides.has_key('input'):
input_list = config_param_overrides['input']
for stim_input in input_list:
cell, start, dur, current = stim_input
c302.add_new_input(nml_doc, cell, start, dur, current, params)
nml_file = target_directory + '/' + reference + '.net.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate, params, muscles_to_include, nml_doc
def setup(parameter_set,
generate=False,
duration=2000,
dt=0.05,
target_directory='examples',
data_reader="SpreadsheetDataReader",
param_overrides={},
verbose=True,
config_param_overrides={}):
exec ('from parameters_%s import ParameterisedModel' % parameter_set, globals())
params = ParameterisedModel()
params.set_bioparameter("unphysiological_offset_current", "0pA", "Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_del", "0 ms", "Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_dur", "2000 ms", "Testing TapWithdrawal", "0")
VA_motors = ["VA%s" % c for c in range_incl(1, 12)]
VB_motors = ["VB%s" % c for c in range_incl(1, 11)]
DA_motors = ["DA%s" % c for c in range_incl(1, 9)]
DB_motors = ["DB%s" % c for c in range_incl(1, 7)]
DD_motors = ["DD%s" % c for c in range_incl(1, 6)]
VD_motors = ["VD%s" % c for c in range_incl(1, 13)]
AS_motors = ["AS%s" % c for c in range_incl(1, 11)]
cells = list(['AVBL', 'AVBR'] + DB_motors + VD_motors + VB_motors + DD_motors)
muscles_to_include = True
cells_to_stimulate = []
cells_to_plot = list(cells)
reference = "c302_%s_FW" % parameter_set
conns_to_include = []
conns_to_exclude = [
'VB2-VB4_GJ',
'VB4-VB2_GJ',
]
conn_polarity_override = {
r'^DB\d+-DD\d+$': 'inh',
r'^VB\d+-VD\d+$': 'inh',
}
conn_number_override = {
'^.+-.+$': 1,
}
input_list = []
'''dur = '250ms'
amp = '3pA'
for muscle_num in range(24):
mdlx = 'MDL0%s' % (muscle_num + 1)
mdrx = 'MDR0%s' % (muscle_num + 1)
#mvlx = 'MVL0%s' % (muscle_num + 1)
#mvrx = 'MVR0%s' % (muscle_num + 1)
if muscle_num >= 9:
mdlx = 'MDL%s' % (muscle_num + 1)
mdrx = 'MDR%s' % (muscle_num + 1)
#mvlx = 'MVL%s' % (muscle_num + 1)
#mvrx = 'MVR%s' % (muscle_num + 1)
startd = '%sms' % (muscle_num * 10)
#startv = '%sms' % ((stim_num * 800 + 400) + muscle_num * 30)
input_list.append((mdlx, startd, dur, amp))
input_list.append((mdrx, startd, dur, amp))'''
input_list.append(('MVR10', '0ms', '150ms', '1pA'))
input_list.append(('MVR11', '0ms', '150ms', '2pA'))
input_list.append(('MVR12', '0ms', '150ms', '3pA'))
input_list.append(('MVR13', '0ms', '150ms', '3pA'))
input_list.append(('MVR14', '0ms', '150ms', '2pA'))
input_list.append(('MVR15', '0ms', '150ms', '1pA'))
input_list.append(('MVL10', '0ms', '150ms', '1pA'))
input_list.append(('MVL11', '0ms', '150ms', '2pA'))
input_list.append(('MVL12', '0ms', '150ms', '3pA'))
input_list.append(('MVL13', '0ms', '150ms', '3pA'))
input_list.append(('MVL14', '0ms', '150ms', '2pA'))
input_list.append(('MVL15', '0ms', '150ms', '1pA'))
input_list.append(('MDL21', '0ms', '250ms', '3pA'))
input_list.append(('MDL22', '0ms', '250ms', '3pA'))
input_list.append(('MDR21', '0ms', '250ms', '3pA'))
input_list.append(('MDR22', '0ms', '250ms', '3pA'))
amp = '4pA'
dur = '250ms'
for stim_num in range(15):
for muscle_num in range(7):
mdlx = 'MDL0%s' % (muscle_num + 1)
mdrx = 'MDR0%s' % (muscle_num + 1)
mvlx = 'MVL0%s' % (muscle_num + 1)
mvrx = 'MVR0%s' % (muscle_num + 1)
if muscle_num >= 9:
mdlx = 'MDL%s' % (muscle_num + 1)
mdrx = 'MDR%s' % (muscle_num + 1)
mvlx = 'MVL%s' % (muscle_num + 1)
mvrx = 'MVR%s' % (muscle_num + 1)
startd = '%sms' % (stim_num * 800 + muscle_num * 30)
startv = '%sms' % ((stim_num * 800 + 400) + muscle_num * 30)
input_list.append((mdlx, startd, dur, amp))
input_list.append((mdrx, startd, dur, amp))
if muscle_num != 6:
input_list.append((mvlx, startv, dur, amp))
input_list.append((mvrx, startv, dur, amp))
d_v_delay = 400
start = 190
motor_dur = '250ms'
input_list.append(('AVBL', '0ms', '%sms'%duration, '15pA'))
input_list.append(('AVBR', '0ms', '%sms'%duration, '15pA'))
input_list.append(('DB1', '%sms'%(start), motor_dur, '3pA'))
input_list.append(('VB1', '%sms'%(start+d_v_delay), motor_dur, '3pA'))
i = start + 2 * d_v_delay
j = start + 3 * d_v_delay
for pulse_num in range(1,15):
input_list.append(('DB1', '%sms'%i, motor_dur, '3pA'))
input_list.append(('VB1', '%sms'%j, motor_dur, '3pA'))
i += d_v_delay * 2
j += d_v_delay * 2
#input_list = []
#input_list.append(('AVBL', '0ms', '1900ms', '15pA'))
#input_list.append(('AVBR', '0ms', '1900ms', '15pA'))
config_param_overrides['input'] = input_list
param_overrides = {
'mirrored_elec_conn_params': {
r'^AVB._to_DB\d+\_GJ$_elec_syn_gbase': '0.001 nS',
r'^AVB._to_VB\d+\_GJ$_elec_syn_gbase': '0.001 nS',
r'^DB\d+_to_DB\d+\_GJ$_elec_syn_gbase': '0.001 nS',
#'^DB\d+_to_DB\d+\_GJ$_elec_syn_p_gbase': '0.08 nS',
#'^DB\d+_to_DB\d+\_GJ$_elec_syn_sigma': '0.2 per_mV',
#'^DB\d+_to_DB\d+\_GJ$_elec_syn_mu': '-20 mV',
r'^VB\d+_to_VB\d+\_GJ$_elec_syn_gbase': '0.001 nS',
#'^VB\d+_to_VB\d+\_GJ$_elec_syn_p_gbase': '0.1 nS',
#'^VB\d+_to_VB\d+\_GJ$_elec_syn_sigma': '0.3 per_mV',
#'^VB\d+_to_VB\d+\_GJ$_elec_syn_mu': '-30 mV',
#'VB2_to_VB4_elec_syn_gbase': '0 nS',
r'^DB\d+_to_VB\d+\_GJ$_elec_syn_gbase': '0 nS',
r'^DB\d+_to_DD\d+\_GJ$_elec_syn_gbase': '0 nS',
r'^VB\d+_to_VD\d+\_GJ$_elec_syn_gbase': '0 nS',
#'^VD\d+_to_DD\d+\_GJ$_elec_syn_gbase': '0 nS',
'DD1_to_MVL08_elec_syn_gbase': '0 nS',
'VD2_to_MDL09_elec_syn_gbase': '0 nS',
},
r'^VB\d+_to_VB\d+$_exc_syn_conductance': '18 nS',
r'^VB\d+_to_VB\d+$_exc_syn_ar': '0.19 per_s',
r'^VB\d+_to_VB\d+$_exc_syn_ad': '73 per_s',
r'^VB\d+_to_VB\d+$_exc_syn_beta': '2.81 per_mV',
r'^VB\d+_to_VB\d+$_exc_syn_vth': '-22 mV',
r'^VB\d+_to_VB\d+$_exc_syn_erev': '10 mV',
r'^DB\d+_to_DB\d+$_exc_syn_conductance': '20 nS',
r'^DB\d+_to_DB\d+$_exc_syn_ar': '0.08 per_s',
r'^DB\d+_to_DB\d+$_exc_syn_ad': '18 per_s',
r'^DB\d+_to_DB\d+$_exc_syn_beta': '0.21 per_mV',
r'^DB\d+_to_DB\d+$_exc_syn_vth': '-10 mV',
r'^DB\d+_to_DB\d+$_exc_syn_erev': '10 mV',
'initial_memb_pot': '-50 mV',
'AVBR_to_DB4_exc_syn_conductance': '0 nS',
#'VB4_to_VB5_exc_syn_conductance': '0 nS',
'AVBL_to_VB2_exc_syn_conductance': '0 nS',
'AVBR_to_VD3_exc_syn_conductance': '0 nS',
#'^DB\d+_to_DD\d+$_exc_syn_conductance': '0 nS',
#'^DD\d+_to_DB\d+$_inh_syn_conductance': '0 nS',
#'^VB\d+_to_VD\d+$_exc_syn_conductance': '0 nS',
#'^VD\d+_to_VB\d+$_inh_syn_conductance': '0 nS',
'DD1_to_VB2_inh_syn_conductance': '0 nS',
'neuron_to_muscle_exc_syn_conductance': '0.5 nS',
r'^DB\d+_to_MDL\d+$_exc_syn_conductance': '0.4 nS',
r'^DB\d+_to_MDR\d+$_exc_syn_conductance': '0.4 nS',
r'^VB\d+_to_MVL\d+$_exc_syn_conductance': '0.6 nS',
r'^VB\d+_to_MVR\d+$_exc_syn_conductance': '0.6 nS',
'neuron_to_muscle_exc_syn_vth': '37 mV',
'neuron_to_muscle_inh_syn_conductance': '0.6 nS',
'neuron_to_neuron_inh_syn_conductance': '0.2 nS',
#'DB2_to_MDL11_exc_syn_conductance': '1 nS',
'AVBR_to_MVL16_exc_syn_conductance': '0 nS',
'ca_conc_decay_time_muscle': '60.8 ms',
'ca_conc_rho_muscle': '0.002338919 mol_per_m_per_A_per_s',
}
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_plot=cells_to_plot,
cells_to_stimulate=cells_to_stimulate,
conns_to_include=conns_to_include,
conns_to_exclude=conns_to_exclude,
conn_polarity_override=conn_polarity_override,
conn_number_override=conn_number_override,
muscles_to_include=muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
data_reader=data_reader,
param_overrides=param_overrides,
verbose=verbose)
#if config_param_overrides.has_key('input'):
# input_list = config_param_overrides['input']
for stim_input in input_list:
cell, start, dur, current = stim_input
c302.add_new_input(nml_doc, cell, start, dur, current, params)
nml_file = target_directory + '/' + reference + '.net.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate, params, muscles_to_include, nml_doc
def setup(parameter_set,
generate=False,
duration=2000,
dt=0.05,
target_directory='examples',
data_reader="UpdatedSpreadsheetDataReader",
param_overrides={},
verbose=True,
config_param_overrides={}):
exec ('from parameters_%s import ParameterisedModel' % parameter_set)
params = ParameterisedModel()
params.set_bioparameter("unphysiological_offset_current", "0pA", "Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_del", "0 ms", "Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_dur", "2000 ms", "Testing TapWithdrawal", "0")
VA_motors = ["VA%s" % c for c in range_incl(1, 12)]
VB_motors = ["VB%s" % c for c in range_incl(1, 11)]
DA_motors = ["DA%s" % c for c in range_incl(1, 9)]
DB_motors = ["DB%s" % c for c in range_incl(1, 7)]
DD_motors = ["DD%s" % c for c in range_incl(1, 6)]
VD_motors = ["VD%s" % c for c in range_incl(1, 13)]
AS_motors = ["AS%s" % c for c in range_incl(1, 11)]
cells = list(['AVBL', 'AVBR'] + VB_motors + DB_motors + VD_motors + DD_motors)
muscles_to_include = False
if config_param_overrides.has_key('muscles_to_include'):
muscles_to_include = config_param_overrides['muscles_to_include']
cells_to_stimulate = []
cells_to_plot = list(cells)
reference = "c302_%s_AVB_VB_DB_VD_DD" % parameter_set
conns_to_include = []
if config_param_overrides.has_key('conns_to_include'):
conns_to_include = config_param_overrides['conns_to_include']
conns_to_exclude = []
if config_param_overrides.has_key('conns_to_exclude'):
conns_to_exclude = config_param_overrides['conns_to_exclude']
conn_polarity_override = {}
if config_param_overrides.has_key('conn_polarity_override'):
conn_polarity_override.update(config_param_overrides['conn_polarity_override'])
conn_number_override = {
}
if config_param_overrides.has_key('conn_number_override'):
conn_number_override.update(config_param_overrides['conn_number_override'])
end = '%sms' % (int(duration) - 100)
input_list = [
('AVBL', '0ms', end, '15pA'),
('AVBR', '0ms', end, '15pA'),
]
if config_param_overrides.has_key('input'):
input_list = config_param_overrides['input']
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_plot=cells_to_plot,
cells_to_stimulate=cells_to_stimulate,
conns_to_include=conns_to_include,
conns_to_exclude=conns_to_exclude,
conn_polarity_override=conn_polarity_override,
conn_number_override=conn_number_override,
muscles_to_include=muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
data_reader=data_reader,
param_overrides=param_overrides,
verbose=verbose)
if config_param_overrides.has_key('input'):
input_list = config_param_overrides['input']
for stim_input in input_list:
cell, start, dur, current = stim_input
c302.add_new_input(nml_doc, cell, start, dur, current, params)
nml_file = target_directory + '/' + reference + '.net.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate, params, muscles_to_include, nml_doc
def setup(parameter_set,
generate=False,
duration=2000,
dt=0.05,
target_directory='examples',
data_reader="SpreadsheetDataReader",
param_overrides={},
verbose=True,
config_param_overrides={}):
exec('from parameters_%s import ParameterisedModel' % parameter_set)
params = ParameterisedModel()
params.set_bioparameter("unphysiological_offset_current", "0pA",
"Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_del", "0 ms",
"Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_dur", "2000 ms",
"Testing TapWithdrawal", "0")
VA_motors = ["VA%s" % c for c in range_incl(1, 12)]
VB_motors = ["VB%s" % c for c in range_incl(1, 11)]
DA_motors = ["DA%s" % c for c in range_incl(1, 9)]
DB_motors = ["DB%s" % c for c in range_incl(1, 7)]
DD_motors = ["DD%s" % c for c in range_incl(1, 6)]
VD_motors = ["VD%s" % c for c in range_incl(1, 13)]
AS_motors = ["AS%s" % c for c in range_incl(1, 11)]
#cells = list(['AVBL', 'AVBR'] + DB_motors + VD_motors + VB_motors + DD_motors + VA_motors + DA_motors + AS_motors)
#cells = list(['AVBL', 'AVBR', 'AVAL', 'AVAR'] + DB_motors + VD_motors + VB_motors + DD_motors + AS_motors + VA_motors + DA_motors)
#cells = list(['AVBL', 'AVBR'] + ['DB2', 'DB5', 'DB6'])
cells = list(['AVBL', 'AVBR'] + DB_motors)
muscles_to_include = True
cells_to_stimulate = []
cells_to_plot = list(cells)
reference = "c302_%s_AVB_DB" % parameter_set
conns_to_include = []
if config_param_overrides.has_key('conns_to_include'):
conns_to_include = config_param_overrides['conns_to_include']
conn_polarity_override = {}
if config_param_overrides.has_key('conn_polarity_override'):
conn_polarity_override.update(
config_param_overrides['conn_polarity_override'])
conn_number_override = {}
if config_param_overrides.has_key('conn_number_override'):
conn_number_override.update(
config_param_overrides['conn_number_override'])
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_plot=cells_to_plot,
cells_to_stimulate=cells_to_stimulate,
conns_to_include=conns_to_include,
conn_polarity_override=conn_polarity_override,
conn_number_override=conn_number_override,
muscles_to_include=muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
data_reader=data_reader,
param_overrides=param_overrides,
verbose=verbose)
end = int(duration) - 100
#for db in DB_motors:
# c302.add_new_sinusoidal_input(nml_doc, cell=db, delay="0ms", duration="1000ms", amplitude="3pA",
# period="700ms", params=params)
c302.add_new_input(nml_doc, "AVBL", "50ms", "%sms" % end, "15pA",
params)
c302.add_new_input(nml_doc, "AVBR", "50ms", "%sms" % end, "15pA",
params)
#c302.add_new_input(nml_doc, "AVBL", "50ms", "850ms", "15pA", params)
#c302.add_new_input(nml_doc, "AVBR", "50ms", "850ms", "15pA", params)
nml_file = target_directory + '/' + reference + '.nml'
writers.NeuroMLWriter.write(
nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate, params, muscles_to_include, nml_doc
cells_to_stimulate = []
reference = "c302_%s_Social"%parameter_set
target_directory='examples'
nml_doc = generate(reference,
params,
cells=cells,
cells_to_stimulate=cells_to_stimulate,
duration=2500,
dt=0.1,
vmin=-72 if parameter_set=='A' else -52,
vmax=-48 if parameter_set=='A' else -28,
validate=(parameter_set!='B'),
target_directory=target_directory)
stim_amplitude = "0.35nA"
add_new_input(nml_doc, "RMGR", "100ms", "200ms", stim_amplitude, params)
add_new_input(nml_doc, "ASHR", "400ms", "200ms", stim_amplitude, params)
add_new_input(nml_doc, "ASKR", "700ms", "200ms", stim_amplitude, params)
add_new_input(nml_doc, "AWBR", "1000ms", "200ms", stim_amplitude, params)
add_new_input(nml_doc, "IL2R", "1300ms", "200ms", stim_amplitude, params)
add_new_input(nml_doc, "RMHR", "1600ms", "200ms", stim_amplitude, params)
add_new_input(nml_doc, "URXR", "1900ms", "200ms", stim_amplitude, params)
nml_file = target_directory+'/'+reference+'.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: "+nml_file)
def setup(parameter_set,
generate=False,
duration=500,
dt=0.05,
target_directory='examples',
data_reader="SpreadsheetDataReader",
param_overrides={},
config_param_overrides={},
verbose=True):
exec('from parameters_%s import ParameterisedModel'%parameter_set, globals())
params = ParameterisedModel()
stim_amplitudes = ["2pA","5pA"]
duration = (len(stim_amplitudes))*1800
params.set_bioparameter("unphysiological_offset_current_del", "50 ms", "Testing IClamp", "0")
exc_pre = "URYDL"
exc_post = "SMDDR"
inh_pre = "VD12"
inh_post = "VB11"
gap_1 = "AIZL"
gap_2 = "ASHL"
moto_pre = "DA1"
muscle_post = "MDL08"
cells = [exc_pre, exc_post, inh_pre, inh_post, moto_pre]
cells_to_stimulate_extra = [exc_pre, inh_pre,moto_pre]
muscles_to_include = [muscle_post]
if parameter_set!='A':
cells.append(gap_1)
cells.append(gap_2)
cells_to_stimulate_extra.append(gap_1)
reference = "c302_%s_Syns"%parameter_set
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_stimulate=[],
muscles_to_include = muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
param_overrides=param_overrides,
verbose=verbose,
data_reader=data_reader)
for i in range(len(stim_amplitudes)):
start = "%sms"%(i*1400 + 500)
for c in cells_to_stimulate_extra:
c302.add_new_input(nml_doc, c, start, "800ms", stim_amplitudes[i], params)
nml_file = target_directory+'/'+reference+'.net.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: "+nml_file)
return cells, cells_to_stimulate_extra, params, [], nml_doc
def setup(parameter_set,
generate=False,
duration=2000,
dt=0.05,
target_directory='examples',
data_reader="SpreadsheetDataReader",
param_overrides={},
verbose=True,
config_param_overrides={}):
exec('from c302.parameters_%s import ParameterisedModel' % parameter_set,
globals())
params = ParameterisedModel()
params.set_bioparameter("unphysiological_offset_current", "0pA",
"Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_del", "0 ms",
"Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_dur", "2000 ms",
"Testing TapWithdrawal", "0")
VA_motors = ["VA%s" % c for c in range_incl(1, 12)]
VB_motors = ["VB%s" % c for c in range_incl(1, 11)]
DA_motors = ["DA%s" % c for c in range_incl(1, 9)]
DB_motors = ["DB%s" % c for c in range_incl(1, 7)]
DD_motors = ["DD%s" % c for c in range_incl(1, 6)]
VD_motors = ["VD%s" % c for c in range_incl(1, 13)]
AS_motors = ["AS%s" % c for c in range_incl(1, 11)]
cells = []
muscles_to_include = True
if config_param_overrides.has_key('muscles_to_include'):
muscles_to_include = config_param_overrides['muscles_to_include']
cells_to_stimulate = []
cells_to_plot = list(cells)
reference = "c302_%s_MuscleTest" % parameter_set
conns_to_include = []
if config_param_overrides.has_key('conns_to_include'):
conns_to_include = config_param_overrides['conns_to_include']
conns_to_exclude = ['^.+-.+$']
if config_param_overrides.has_key('conns_to_exclude'):
conns_to_exclude = config_param_overrides['conns_to_exclude']
conn_polarity_override = {}
if config_param_overrides.has_key('conn_polarity_override'):
conn_polarity_override.update(
config_param_overrides['conn_polarity_override'])
conn_number_override = {}
if config_param_overrides.has_key('conn_number_override'):
conn_number_override.update(
config_param_overrides['conn_number_override'])
param_overrides = {
'ca_conc_decay_time_muscle': '60 ms',
'ca_conc_rho_muscle': '0.002138919 mol_per_m_per_A_per_s',
}
end = '%sms' % (int(duration) - 100)
input_list = []
#input_list.append(('MDL02', '0ms', '250ms', '3pA'))
#input_list.append(('MDL03', '0ms', '250ms', '3pA'))
#input_list.append(('MDR02', '0ms', '250ms', '3pA'))
#input_list.append(('MDR03', '0ms', '250ms', '3pA'))
input_list.append(('MVR10', '0ms', '250ms', '1pA'))
input_list.append(('MVR11', '0ms', '250ms', '2pA'))
input_list.append(('MVR12', '0ms', '250ms', '3pA'))
input_list.append(('MVR13', '0ms', '250ms', '3pA'))
input_list.append(('MVR14', '0ms', '250ms', '2pA'))
input_list.append(('MVR15', '0ms', '250ms', '1pA'))
input_list.append(('MVL10', '0ms', '250ms', '1pA'))
input_list.append(('MVL11', '0ms', '250ms', '2pA'))
input_list.append(('MVL12', '0ms', '250ms', '3pA'))
input_list.append(('MVL13', '0ms', '250ms', '3pA'))
input_list.append(('MVL14', '0ms', '250ms', '2pA'))
input_list.append(('MVL15', '0ms', '250ms', '1pA'))
input_list.append(('MDL21', '0ms', '250ms', '3pA'))
input_list.append(('MDL22', '0ms', '250ms', '3pA'))
input_list.append(('MDR21', '0ms', '250ms', '3pA'))
input_list.append(('MDR22', '0ms', '250ms', '3pA'))
for stim_num in range(5):
for muscle_num in range(24):
mdlx = 'MDL0%s' % (muscle_num + 1)
mdrx = 'MDR0%s' % (muscle_num + 1)
mvlx = 'MVL0%s' % (muscle_num + 1)
mvrx = 'MVR0%s' % (muscle_num + 1)
if muscle_num >= 9:
mdlx = 'MDL%s' % (muscle_num + 1)
mdrx = 'MDR%s' % (muscle_num + 1)
mvlx = 'MVL%s' % (muscle_num + 1)
if muscle_num != 23:
mvrx = 'MVR%s' % (muscle_num + 1)
startd = '%sms' % (stim_num * 1000 + muscle_num * 50)
startv = '%sms' % ((stim_num * 1000 + 500) + muscle_num * 50)
dur = '250ms'
amp = '3pA'
input_list.append((mdlx, startd, dur, amp))
input_list.append((mdrx, startd, dur, amp))
input_list.append((mvlx, startv, dur, amp))
input_list.append((mvrx, startv, dur, amp))
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_plot=cells_to_plot,
cells_to_stimulate=cells_to_stimulate,
conns_to_include=conns_to_include,
conns_to_exclude=conns_to_exclude,
conn_polarity_override=conn_polarity_override,
conn_number_override=conn_number_override,
muscles_to_include=muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
data_reader=data_reader,
param_overrides=param_overrides,
verbose=verbose)
if 'input' in config_param_overrides:
input_list = config_param_overrides['input']
for stim_input in input_list:
cell, start, dur, current = stim_input
c302.add_new_input(nml_doc, cell, start, dur, current, params)
nml_file = target_directory + '/' + reference + '.net.nml'
writers.NeuroMLWriter.write(
nml_doc, nml_file) # Write over network file written above...
c302.print_("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate, params, muscles_to_include, nml_doc
def setup(parameter_set,
generate=False,
duration=400,
dt=0.05,
target_directory='examples',
data_reader="UpdatedSpreadsheetDataReader",
param_overrides={},
verbose=True):
exec('from parameters_%s import ParameterisedModel' % parameter_set)
params = ParameterisedModel()
params.set_bioparameter("unphysiological_offset_current", "0pA",
"Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_del", "0 ms",
"Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_dur", "2000 ms",
"Testing TapWithdrawal", "0")
neurons = [
'AVAL', 'AVAR', 'AVBL', 'AVBR', 'PVCL', 'PVCR', 'AVDL', 'AVDR', 'DVA',
'PVDL', 'PVDR', 'PLML', 'PLMR', 'AVM', 'ALML', 'ALMR'
]
motors = []
motors += [
'VA1', 'VA2', 'VA3', 'VA4', 'VA5', 'VA6', 'VA7', 'VA8', 'VA9', 'VA10',
'VA11', 'VA12'
]
motors += [
'VB1', 'VB2', 'VB3', 'VB4', 'VB5', 'VB6', 'VB7', 'VB8', 'VB9', 'VB10',
'VB11'
]
motors += ['DA1', 'DA2', 'DA3', 'DA4', 'DA5', 'DA6', 'DA7', 'DA8', 'DA9']
motors += ['DB1', 'DB2', 'DB3', 'DB4', 'DB5', 'DB6', 'DB7']
motors += ['DD1', 'DD2', 'DD3', 'DD4', 'DD5', 'DD6']
motors += [
'VD1', 'VD2', 'VD3', 'VD4', 'VD5', 'VD6', 'VD7', 'VD8', 'VD9', 'VD10',
'VD11', 'VD12', 'VD13'
]
muscles_to_include = False
muscles_to_include = True # ALL muscles
#muscles_to_include = ['MVL01', 'MVL10']
cells = list(neurons)
cells += motors
cells_to_plot = list(neurons)
cells += [ # 'AS1', 'AS10', 'AS11', 'AS2', 'AS3', 'AS4', 'AS5', 'AS6', 'AS7', 'AS8', 'AS9',
# 'AVFL', 'AVFR', 'AVKR', 'AVL',
# 'CEPVL', 'CEPVR',
#
#
# 'DVB',
# 'HSNL', 'HSNR',
# 'IL1DL', 'IL1DR', 'IL1L', 'IL1R', 'IL1VL', 'IL1VR',
# 'PDA', 'PDB',
# 'PVNL', 'PVNR',
# 'RID', 'RIML', 'RIMR', 'RIVL', 'RIVR',
# 'RMDDL', 'RMDDR', 'RMDL', 'RMDR', 'RMDVL', 'RMDVR', 'RMED', 'RMEL', 'RMER', 'RMEV', 'RMFL', 'RMGL', 'RMGR',
# 'RMHL', 'RMHR',
# 'SMBDL', 'SMBDR', 'SMBVL', 'SMBVR', 'SMDDL', 'SMDDR', 'SMDVL', 'SMDVR',
# 'URADL', 'URADR', 'URAVL', 'URAVR',
# 'VC1', 'VC2', 'VC3', 'VC4', 'VC5', 'VC6',
]
# cells = None
#cells_to_stimulate = ['PLML', 'PLMR', 'AVM']
cells_to_stimulate = []
#cells_to_plot = ['AVAL', 'AVAR', 'AVBL', 'AVBR', 'PLML', 'PLMR', 'AVM', 'ALML', 'ALMR', 'PVDL', 'PVDR', 'AVDL', 'AVDR']
#cells_to_plot = ['PLML', 'AVM', 'AVBL', 'AVAL']
#cells_to_plot = cells
#cells_to_plot += motors
reference = "c302_%s_TapWithdrawal" % parameter_set
conn_polarity_override = {
'ALML-ALML': 'inh',
'ALML-PVCL': 'inh',
'ALML-PVCR': 'inh',
'ALML-AVDR': 'exc',
'ALMR-PVCR': 'inh',
'AVM-PVCL': 'inh',
'AVM-PVCR': 'inh',
'AVM-AVBL': 'inh',
'AVM-AVBR': 'inh',
'AVM-AVDL': 'exc',
'AVM-AVDR': 'exc',
'PVDL-PVDR': 'inh',
'PVDL-PVCL': 'exc',
'PVDL-PVCR': 'exc',
'PVDL-AVAL': 'inh',
'PVDL-AVAR': 'inh',
'PVDL-AVDL': 'inh',
'PVDL-AVDR': 'inh',
'PVDR-PVDL': 'inh',
'PVDR-DVA': 'exc',
'PVDR-PVCL': 'exc',
'PVDR-PVCR': 'exc',
'PVDR-AVAL': 'inh',
'PVDR-AVAR': 'inh',
'PVDR-AVDL': 'inh',
'DVA-PVCL': 'inh', #
'DVA-PVCR': 'inh', #
'DVA-AVAL': 'inh',
'DVA-AVAR': 'inh',
'DVA-AVBL': 'inh', #
'DVA-AVBR': 'inh', #
'DVA-AVDR': 'inh',
'PVCL-DVA': 'exc',
'PVCL-PVCL': 'inh',
'PVCL-PVCR': 'inh',
'PVCL-AVAL': 'inh',
'PVCL-AVAR': 'inh',
'PVCL-AVBL': 'exc',
'PVCL-AVBR': 'exc',
'PVCL-AVDL': 'inh',
'PVCL-AVDR': 'inh',
'PVCR-PVDL': 'inh',
'PVCR-PVDR': 'inh',
'PVCR-DVA': 'exc',
'PVCR-PVCL': 'inh',
'PVCR-AVAL': 'inh',
'PVCR-AVAR': 'inh',
'PVCR-AVBL': 'exc',
'PVCR-AVBR': 'exc',
'PVCR-AVDL': 'inh',
'PVCR-AVDR': 'inh',
'AVAL-PVCL': 'inh',
'AVAL-PVCR': 'inh',
'AVAL-AVAR': 'inh',
'AVAL-AVBL': 'inh',
'AVAL-AVDL': 'inh',
'AVAL-AVDR': 'inh',
'AVAR-PVCL': 'inh',
'AVAR-PVCR': 'inh',
'AVAR-AVAL': 'inh',
'AVAR-AVBL': 'inh',
'AVAR-AVBR': 'inh',
'AVAR-AVDL': 'inh',
'AVAR-AVDR': 'inh',
'AVBL-DVA': 'inh',
'AVBL-PVCR': 'inh',
'AVBL-AVAL': 'inh',
'AVBL-AVAR': 'inh',
'AVBL-AVBR': 'inh',
'AVBL-AVDR': 'inh',
'AVBR-AVAL': 'inh',
'AVBR-AVAR': 'inh',
'AVBR-AVBL': 'inh',
'AVBR-AVDL': 'inh',
'AVDL-PVCL': 'inh',
'AVDL-AVAL': 'exc',
'AVDL-AVAR': 'exc',
'AVDL-AVDR': 'exc',
'AVDR-PVCR': 'inh',
'AVDR-AVAL': 'exc',
'AVDR-AVAR': 'exc',
'AVDR-AVBL': 'inh',
'AVDR-AVDL': 'exc',
'DA9-DVA': 'inh',
'DVA-DA2': 'inh',
'PVCL-DA5': 'inh',
'PVCL-DA8': 'inh',
'PVCR-DA2': 'inh',
'PVCR-DA4': 'inh',
'PVCR-DA5': 'inh',
'PVCR-DA7': 'inh',
'AVBL-DA5': 'inh',
'AVBL-DA7': 'inh',
'AVBR-DA3': 'inh',
'AVBR-DA4': 'inh',
'AVBR-DA7': 'inh',
'AVDL-DA1': 'inh',
'AVDL-DA2': 'inh',
'AVDL-DA3': 'inh',
'AVDL-DA4': 'inh',
'AVDL-DA5': 'inh',
'AVDL-DA8': 'inh',
'AVDR-DA1': 'inh',
'AVDR-DA2': 'inh',
'AVDR-DA3': 'inh',
'AVDR-DA4': 'inh',
'AVDR-DA5': 'inh',
'AVDR-DA8': 'inh',
'DB1-DA1': 'inh',
'DB1-DA2': 'inh',
'DB2-DA2': 'inh',
'DB2-DA3': 'inh',
'DB2-DA4': 'inh',
'DB3-DA4': 'inh',
'DB3-DA5': 'inh',
'DB4-DA5': 'inh',
'DB5-DA6': 'inh',
'DB5-DA7': 'inh',
'DB5-DA8': 'inh',
'DB6-DA8': 'inh',
'DB6-DA9': 'inh',
'DB7-DA9': 'inh',
'AVAR-DB2': 'inh',
'AVAR-DB3': 'inh',
'AVAL-DB7': 'inh',
'VA12-DB7': 'inh',
'DA1-DB1': 'inh',
'DA2-DA3': 'inh',
'DA2-DB1': 'inh',
'DA3-DB3': 'inh',
'DA4-DB2': 'inh',
'DA5-DB4': 'inh',
'DA6-DB5': 'inh',
'DA7-DB6': 'inh',
'DA8-DB7': 'inh',
'DA9-DB7': 'inh',
'DVA-VA2': 'inh',
'DVA-VA6': 'inh',
'DVA-VA8': 'inh',
'DVA-VA12': 'inh',
'PVCL-VA11': 'inh',
'PVDR-VA9': 'inh',
'PVDR-VA12': 'inh',
'AVBL-VA2': 'inh',
'AVBL-VA10': 'inh',
'AVBR-VA3': 'inh',
'AVBR-VA4': 'inh',
'AVDL-VA3': 'inh',
'AVDL-VA5': 'inh',
'AVDL-VA10': 'inh',
'AVDL-VA12': 'inh',
'AVDR-VA2': 'inh',
'AVDR-VA3': 'inh',
'AVDR-VA5': 'inh',
'AVDR-VA11': 'inh',
'VB1-VA1': 'inh',
'VB1-VA2': 'inh',
'VB1-VA3': 'inh',
'VB1-VA4': 'inh',
'VB2-VA2': 'inh',
'VB2-VA3': 'inh',
'VB3-VA4': 'inh',
'VB3-VA5': 'inh',
'VB4-VA4': 'inh',
'VB4-VA5': 'inh',
'VB5-VA6': 'inh',
'VB6-VA7': 'inh',
'VB6-VA8': 'inh',
'VB7-VA9': 'inh',
'VB7-VA10': 'inh',
'VB8-VA11': 'inh',
'VB9-VA11': 'inh',
'VB10-VA11': 'inh',
'VB11-VA12': 'inh',
'VB11-PVCR': 'inh',
'VB4-VB5': 'inh',
'VB11-VA12': 'inh',
'VA2-VB1': 'inh',
'VA2-VB2': 'inh',
'VA3-VB2': 'inh',
'VA3-VB3': 'inh',
'VA4-AVDL': 'inh',
'VA4-VB3': 'inh',
'VA4-VB4': 'inh',
'VA5-VB4': 'inh',
'VA6-VB4': 'inh',
'VA6-VB5': 'inh',
'VA7-VB6': 'inh',
'VA8-VB6': 'inh',
'VA9-VB7': 'inh',
'VA9-VB8': 'inh',
'VA10-VB8': 'inh',
'VA10-VB9': 'inh',
'VA11-VB10': 'inh',
'VA12-PVCL': 'inh',
'VA12-PVCR': 'inh',
'VA12-DB7': 'inh',
'VA12-VB11': 'inh',
'AVM-VB3': 'inh',
#'VB4-VB5':'exc',
#'DD1-DA2':'inh',
#'DD1-VB2':'inh',
#'DD2-DA3':'inh',
#'DD3-DA5':'inh',
}
conn_number_override = {
#'PVCL-AVDL':7*0.1,
#'PVCL-AVDR':11*0.1,
#'PVCR-AVDL':16*0.1,
#'PVCR-AVDR':6*0.1,
#'PVCR-AVDR_GJ':2 * 0.01,
#'AVDR-PVCR_GJ':2 * 0.01,
'PVCL-AVAL_GJ': 5 * 0.01,
'AVAL-PVCL_GJ': 5 * 0.01,
'PVCL-AVAR_GJ': 10 * 0.01,
'AVAR-PVCL_GJ': 10 * 0.01,
'PVCR-AVAL_GJ': 15 * 0.01,
'AVAL-PVCR_GJ': 15 * 0.01,
'PVCR-AVAR_GJ': 22 * 0.01,
'AVAR-PVCR_GJ': 22 * 0.01,
'PVCL-PLML_GJ': 4 * 0.01,
'PVCR-PLMR_GJ': 8 * 0.01,
#'AVDL-AVM_GJ':8 * 0.01,
#'ALML-AVM_GJ':1 * 0.01,
#'ALMR-AVM_GJ':1 * 0.01,
#'AVDR-ALMR_GJ':1 * 0.01,
# 'AVDL-AVAL':37*0.1,
# 'AVDL-AVAR':37*0.1,
# 'AVDR-AVAL':41*0.1,
# 'AVDR-AVAR':52*0.1,
# 'AVDL-AVAL_GJ':7*0.1,
# 'AVAL-AVDL_GJ':7*0.1,
# 'AVDL-AVAR_GJ':2*0.1,
# 'AVAR-AVDL_GJ':2*0.1,
# 'AVDR-AVAL_GJ':9*0.1,
# 'AVAL-AVDR_GJ':9*0.1,
# 'AVDR-AVAR_GJ':15*0.1,
# 'AVAR-AVDR_GJ':15*0.1,
# 'ALMR-AVDR_GJ':2*5,
# 'AVDR-ALMR_GJ':2*5,
'AVAR-AVBL_GJ': 3 * 0.01,
'AVBL-AVAR_GJ': 3 * 0.01,
# 'AVAR-AVAL_GJ':18*2,
# 'AVAL-AVAR_GJ':18*2,
'PVDL-AVAR_GJ': 4 * 0.01,
'AVAR-PVDL_GJ': 4 * 0.01,
'PVDR-AVAL_GJ': 6 * 0.01,
'AVAL-PVDR_GJ': 6 * 0.01,
'AVBL-VA11_GJ': 1 * 0.01,
'VA11-AVBL_GJ': 1 * 0.01,
'AVBR-VA11_GJ': 3 * 0.01,
'VA11-AVBR_GJ': 3 * 0.01,
'PVCR-VA11_GJ': 3 * 0.01,
'VA11-PVCR_GJ': 3 * 0.01,
'DVA-VA11_GJ': 1 * 0.01,
'VA11-DVA_GJ': 1 * 0.01,
'PVCL-VA12_GJ': 18 * 0.01,
'VA12-PVCL_GJ': 18 * 0.01,
'PVCR-VA12_GJ': 8 * 0.01,
'VA12-PVCR_GJ': 8 * 0.01,
'AVAL-VB11_GJ': 2 * 0.01,
'VB11-AVAL_GJ': 2 * 0.01,
'PVCL-DA4_GJ': 1 * 0.01,
'DA4-PVCL_GJ': 1 * 0.01,
'PVCL-DA7_GJ': 1 * 0.01,
'DA7-PVCL_GJ': 1 * 0.01,
'PVCR-DA7_GJ': 3 * 0.01,
'DA7-PVCR_GJ': 3 * 0.01,
'PVCL-DA8_GJ': 17 * 0.01,
'DA8-PVCL_GJ': 17 * 0.01,
'PVCR-DA8_GJ': 1 * 0.01,
'DA8-PVCR_GJ': 1 * 0.01,
'DVA-DA9_GJ': 3 * 0.01,
'DA9-DVA_GJ': 3 * 0.01,
'PVCR-DA9_GJ': 3 * 0.01,
'DA9-PVCR_GJ': 3 * 0.01,
'DB7-VA10_GJ': 1 * 0.01,
'VA10-DB7_GJ': 1 * 0.01,
'VA4-VB3_GJ': 1 * 0.01,
'VB3-VA4_GJ': 1 * 0.01,
'VA10-DB7_GJ': 1 * 0.01,
'DB7-VA10_GJ': 1 * 0.01,
'VA11-VB10_GJ': 3 * 0.01,
'VB10-VA11_GJ': 3 * 0.01,
'VA12-VB11_GJ': 7 * 0.01,
'VB11-VA12_GJ': 7 * 0.01,
'VB3-VA4_GJ': 1 * 0.01,
'VA4-VB3_GJ': 1 * 0.01,
'VB10-VA11_GJ': 3 * 0.01,
'VA11-VB10_GJ': 3 * 0.01,
'VB11-DA9_GJ': 7 * 0.01,
'DA9-VB11_GJ': 7 * 0.01,
}
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_plot=cells_to_plot,
cells_to_stimulate=cells_to_stimulate,
conn_polarity_override=conn_polarity_override,
conn_number_override=conn_number_override,
muscles_to_include=muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
data_reader=data_reader,
param_overrides=param_overrides,
verbose=verbose)
stim_amplitude = "6pA"
# stim_amplitude = "5.135697186048022pA"
c302.add_new_input(nml_doc, "AVM", "10ms", "50ms", "5pA", params)
c302.add_new_input(nml_doc, "ALML", "10ms", "50ms", "5pA", params)
c302.add_new_input(nml_doc, "ALMR", "10ms", "50ms", "5pA", params)
c302.add_new_input(nml_doc, "PLML", "200ms", "50ms", "5pA", params)
c302.add_new_input(nml_doc, "PLMR", "200ms", "50ms", "5pA", params)
nml_file = target_directory + '/' + reference + '.nml'
writers.NeuroMLWriter.write(
nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate, params, muscles_to_include
parameter_set = sys.argv[1] if len(sys.argv)==2 else 'A'
exec('from parameters_%s import ParameterisedModel'%parameter_set)
params = ParameterisedModel()
cells = ["URYDL", "SMDDR", "ADAL", "RIML", "IL2VL", "RIPL"]
cells_to_stimulate = []
reference = "c302_%s_MultiSyns"%parameter_set
target_directory='examples'
nml_doc = generate(reference,
params,
cells=cells,
cells_to_stimulate=cells_to_stimulate,
duration=1000,
dt=0.1,
validate=(parameter_set!='B'),
target_directory=target_directory)
stim_amplitude = "0.35nA"
add_new_input(nml_doc, "URYDL", "100ms", "200ms", stim_amplitude, params)
add_new_input(nml_doc, "ADAL", "400ms", "200ms", stim_amplitude, params)
add_new_input(nml_doc, "IL2VL", "700ms", "200ms", stim_amplitude, params)
nml_file = target_directory+'/'+reference+'.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: "+nml_file)
def setup(parameter_set,
generate=False,
duration=2000,
dt=0.05,
target_directory='examples',
data_reader="UpdatedSpreadsheetDataReader",
param_overrides={},
verbose=True,
config_param_overrides={}):
exec('from parameters_%s import ParameterisedModel' % parameter_set)
params = ParameterisedModel()
params.set_bioparameter("unphysiological_offset_current", "0pA",
"Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_del", "0 ms",
"Testing TapWithdrawal", "0")
params.set_bioparameter("unphysiological_offset_current_dur", "2000 ms",
"Testing TapWithdrawal", "0")
VA_motors = ["VA%s" % c for c in range_incl(1, 12)]
VB_motors = ["VB%s" % c for c in range_incl(1, 11)]
DA_motors = ["DA%s" % c for c in range_incl(1, 9)]
DB_motors = ["DB%s" % c for c in range_incl(1, 7)]
DD_motors = ["DD%s" % c for c in range_incl(1, 6)]
VD_motors = ["VD%s" % c for c in range_incl(1, 13)]
AS_motors = ["AS%s" % c for c in range_incl(1, 11)]
cells = list(['AVBL', 'AVBR'] + VB_motors + DB_motors + VD_motors +
DD_motors)
muscles_to_include = False
if config_param_overrides.has_key('muscles_to_include'):
muscles_to_include = config_param_overrides['muscles_to_include']
cells_to_stimulate = []
cells_to_plot = list(cells)
reference = "c302_%s_AVB_VB_DB_VD_DD" % parameter_set
conns_to_include = []
if config_param_overrides.has_key('conns_to_include'):
conns_to_include = config_param_overrides['conns_to_include']
conns_to_exclude = []
if config_param_overrides.has_key('conns_to_exclude'):
conns_to_exclude = config_param_overrides['conns_to_exclude']
conn_polarity_override = {}
if config_param_overrides.has_key('conn_polarity_override'):
conn_polarity_override.update(
config_param_overrides['conn_polarity_override'])
conn_number_override = {}
if config_param_overrides.has_key('conn_number_override'):
conn_number_override.update(
config_param_overrides['conn_number_override'])
end = '%sms' % (int(duration) - 100)
input_list = [
('AVBL', '0ms', end, '15pA'),
('AVBR', '0ms', end, '15pA'),
]
if config_param_overrides.has_key('input'):
input_list = config_param_overrides['input']
nml_doc = None
if generate:
nml_doc = c302.generate(reference,
params,
cells=cells,
cells_to_plot=cells_to_plot,
cells_to_stimulate=cells_to_stimulate,
conns_to_include=conns_to_include,
conns_to_exclude=conns_to_exclude,
conn_polarity_override=conn_polarity_override,
conn_number_override=conn_number_override,
muscles_to_include=muscles_to_include,
duration=duration,
dt=dt,
target_directory=target_directory,
data_reader=data_reader,
param_overrides=param_overrides,
verbose=verbose)
if config_param_overrides.has_key('input'):
input_list = config_param_overrides['input']
for stim_input in input_list:
cell, start, dur, current = stim_input
c302.add_new_input(nml_doc, cell, start, dur, current, params)
nml_file = target_directory + '/' + reference + '.net.nml'
writers.NeuroMLWriter.write(
nml_doc, nml_file) # Write over network file written above...
print("(Re)written network file to: " + nml_file)
return cells, cells_to_stimulate, params, muscles_to_include, nml_doc
