def create_PSTH(folder, suffix, contains, outName):
'''
load all traces, compute spike times
and create raster plots
'''
fnames = []
scan_directory2(folder, fnames, suffix, contains)
print 'Creating PSTH from %d files' % len(fnames)
tOffset = 100.0
tStop = 345.0
allSpikeTimes = []
for n in range(len(fnames)):
fname = fnames[n]
print 'Loading traces from file %s' % fname
data = np.loadtxt(fname, skiprows=1, unpack=True)
t = data[0]
for i in range(1, len(data)):
v = data[i]
spikeTimes = sca.simple_spike_detection(t, v)
allSpikeTimes.append(spikeTimes)
binWidth = 1.0
hist, bins = sca.PSTH_from_spike_times(allSpikeTimes, binWidth, tOffset,
tStop)
offset = 0.5 * (bins[1] - bins[0])
fig = plt.figure(2 * n + 2)
plt.bar(bins[:-1], hist, color='b', width=binWidth)
plt.xlabel('t [ms]')
plt.ylabel('AP/stim')
plt.xlim([tOffset, tStop])
outName += '_PSTH_total_%.1fms' % binWidth
plt.savefig(outName + '.pdf')
scp.write_PSTH(outName + '.csv', hist, bins)
def reverse_correlation_synaptic_input(folder, contains, vmSuffix,
synapseSuffix):
'''
load all traces, compute spike times
and create raster plots
'''
excTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM')
inhTypes = ('L1','L23Trans','L45Sym','L45Peak','L56Trans',\
'SymLocal1','SymLocal2','SymLocal3','SymLocal4','SymLocal5','SymLocal6')
cellTypeColorMap = {'L2': 'dodgerblue', 'L34': 'blue', 'L4py': 'palegreen',\
'L4sp': 'green', 'L4ss': 'lime', 'L5st': 'yellow', 'L5tt': 'orange',\
'L6cc': 'indigo', 'L6ccinv': 'violet', 'L6ct': 'magenta', 'VPM': 'black',\
'INH': 'grey', 'EXC': 'red'}
plotTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM', 'EXC', 'INH')
vmNames = []
# scan_directory(folder, vmNames, vmSuffix)
scan_directory2(folder, vmNames, vmSuffix, contains)
synNames = []
# scan_directory(folder, synNames, synapseSuffix)
scan_directory2(folder, synNames, synapseSuffix, contains)
print 'Computing reverse correlation of synaptic input from %d files' % len(
synNames)
# look at spikes in early phase (0-20ms)
# and late phase (20-50ms) separately
reverseTimes = {}
reverseTimesProx = {}
reverseTimesDistal = {}
reverseTimesEarly = {}
reverseTimesEarlyProx = {}
reverseTimesEarlyDistal = {}
reverseTimesLate = {}
reverseTimesLateProx = {}
reverseTimesLateDistal = {}
for cellType in plotTypes:
reverseTimes[cellType] = []
reverseTimesProx[cellType] = []
reverseTimesDistal[cellType] = []
reverseTimesEarly[cellType] = []
reverseTimesEarlyProx[cellType] = []
reverseTimesEarlyDistal[cellType] = []
reverseTimesLate[cellType] = []
reverseTimesLateProx[cellType] = []
reverseTimesLateDistal[cellType] = []
tOffset = 100.0
tStim = 245.0
tStimWindow = 50.0
earlyWindow = 25.0
correlationWindow = 50.0
binWidth = 1.0
for n in range(len(vmNames)):
fname = vmNames[n]
pathName = fname[:fname.rfind('/')]
print 'pathName = %s' % pathName
print 'Loading traces from file %s' % fname
data = np.loadtxt(fname, skiprows=1, unpack=True)
t = data[0]
for i in range(1, len(data)):
# for i in range(1,10):
print 'Computing reverse correlation for spikes in trial %d of %d\r' % (
i, len(data) - 1),
sys.stdout.flush()
trialNr = i - 1
synTrialStr = 'simulation_run%04d_synapses.csv' % trialNr
synTrialFile = ''
for name in synNames:
if synTrialStr in name and pathName in name:
synTrialFile = name
break
if synTrialFile == '':
errstr = 'Could not find synapse activation file for trial nr. %d' % trialNr
raise RuntimeError(errstr)
activeSyns = scp.read_complete_synapse_activation_file(
synTrialFile)
synapseTimes = {}
synapseTimesProx = {}
synapseTimesDistal = {}
for excType in excTypes:
synapseTimes[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesProx[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesDistal[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimes['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesProx['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesDistal['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimes['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
synapseTimesProx['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
synapseTimesDistal['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
for synType in activeSyns.keys():
preCellType = synType.split('_')[0]
for excType in excTypes:
if excType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
synapseTimes[excType][structure].extend(synTimes)
synapseTimes[excType]['Total'].extend(synTimes)
synapseTimes['EXC'][structure].extend(synTimes)
synapseTimes['EXC']['Total'].extend(synTimes)
if somaDist < 500.0:
synapseTimesProx[excType][structure].extend(
synTimes)
synapseTimesProx[excType]['Total'].extend(
synTimes)
synapseTimesProx['EXC'][structure].extend(
synTimes)
synapseTimesProx['EXC']['Total'].extend(
synTimes)
else:
synapseTimesDistal[excType][structure].extend(
synTimes)
synapseTimesDistal[excType]['Total'].extend(
synTimes)
synapseTimesDistal['EXC'][structure].extend(
synTimes)
synapseTimesDistal['EXC']['Total'].extend(
synTimes)
for inhType in inhTypes:
if inhType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
synapseTimes['INH'][structure].extend(synTimes)
synapseTimes['INH']['Total'].extend(synTimes)
if somaDist < 500.0:
synapseTimesProx['INH'][structure].extend(
synTimes)
synapseTimesProx['INH']['Total'].extend(
synTimes)
else:
synapseTimesDistal['INH'][structure].extend(
synTimes)
synapseTimesDistal['INH']['Total'].extend(
synTimes)
v = data[i]
trialSpikeTimes = sca.simple_spike_detection(t, v)
for tSpike in trialSpikeTimes:
#evoked
if tSpike <= tStim or tSpike > tStim + tStimWindow:
#ongoing
#if tSpike < tOffset or tSpike > tStim:
continue
for cellType in plotTypes:
reverseTimes[cellType].append([])
reverseTimesProx[cellType].append([])
reverseTimesDistal[cellType].append([])
if tStim <= tSpike < tStim + earlyWindow:
reverseTimesEarly[cellType].append([])
reverseTimesEarlyProx[cellType].append([])
reverseTimesEarlyDistal[cellType].append([])
else:
reverseTimesLate[cellType].append([])
reverseTimesLateProx[cellType].append([])
reverseTimesLateDistal[cellType].append([])
synTimes = synapseTimes[cellType]['Total']
synTimesProx = synapseTimesProx[cellType]['Total']
synTimesDistal = synapseTimesDistal[cellType]['Total']
for tSyn in synTimes:
# for tSyn in synTimesDistal:
if tSpike - correlationWindow <= tSyn < tSpike:
reverseTimes[cellType][-1].append(tSyn - tSpike)
if tStim <= tSpike < tStim + earlyWindow:
reverseTimesEarly[cellType][-1].append(tSyn -
tSpike)
else:
reverseTimesLate[cellType][-1].append(tSyn -
tSpike)
for tSyn in synTimesProx:
if tSpike - correlationWindow <= tSyn < tSpike:
reverseTimesProx[cellType][-1].append(tSyn -
tSpike)
if tStim <= tSpike < tStim + earlyWindow:
reverseTimesEarlyProx[cellType][-1].append(
tSyn - tSpike)
else:
reverseTimesLateProx[cellType][-1].append(
tSyn - tSpike)
for tSyn in synTimesDistal:
if tSpike - correlationWindow <= tSyn < tSpike:
reverseTimesDistal[cellType][-1].append(tSyn -
tSpike)
if tStim <= tSpike < tStim + earlyWindow:
reverseTimesEarlyDistal[cellType][-1].append(
tSyn - tSpike)
else:
reverseTimesLateDistal[cellType][-1].append(
tSyn - tSpike)
#fig.add_subplot(2,1,1)
#spikes = [i for time in trialSpikeTimes]
#ax.append(plt.plot(trialSpikeTimes, spikes, 'k|'))
print ''
ax = []
fig = plt.figure(1)
for cellType in plotTypes:
hist, bins = sca.PSTH_from_spike_times(reverseTimes[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 1)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
hist, bins = sca.PSTH_from_spike_times(reverseTimesEarly[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 2)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
hist, bins = sca.PSTH_from_spike_times(reverseTimesLate[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 3)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
#fig.add_subplot(3,1,1)
plt.xlabel('t [ms]')
plt.ylabel('All spikes')
plt.xlim([-correlationWindow, 0.0])
#fig.add_subplot(3,1,2)
#plt.xlabel('t [ms]')
#plt.ylabel('Early spikes')
#plt.xlim([-correlationWindow, 0.0])
#fig.add_subplot(3,1,3)
#plt.xlabel('t [ms]')
#plt.ylabel('Late spikes')
#plt.xlim([-correlationWindow, 0.0])
fig = plt.figure(2)
for cellType in plotTypes:
hist, bins = sca.PSTH_from_spike_times(reverseTimesProx[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 1)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
hist, bins = sca.PSTH_from_spike_times(reverseTimesEarlyProx[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 2)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
hist, bins = sca.PSTH_from_spike_times(reverseTimesLateProx[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 3)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
plt.xlabel('t [ms]')
plt.ylabel('All spikes (proximal syns)')
plt.xlim([-correlationWindow, 0.0])
fig = plt.figure(3)
for cellType in plotTypes:
hist, bins = sca.PSTH_from_spike_times(reverseTimesDistal[cellType],
binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 1)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
hist, bins = sca.PSTH_from_spike_times(
reverseTimesEarlyDistal[cellType], binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 2)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
hist, bins = sca.PSTH_from_spike_times(
reverseTimesLateDistal[cellType], binWidth, -correlationWindow,
0.0)
offset = 0.5 * (bins[1] - bins[0])
fig.add_subplot(3, 1, 3)
ax.append(
plt.plot(bins[:-1] + offset,
hist,
color=cellTypeColorMap[cellType],
label=cellType,
linewidth=2))
plt.xlabel('t [ms]')
plt.ylabel('All spikes (distal syns)')
plt.xlim([-correlationWindow, 0.0])
# outName = fname[:-4]
# outName += '_spike_raster_plot.pdf'
# plt.savefig(outName)
plt.show()
def create_active_synapse_histogram_spike_no_spike(folder, suffix, vmSuffix,
contains, outName):
'''
load all traces, compute spike times
and create raster plots
'''
excTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM')
inhTypes = ('L1','L23Trans','L45Sym','L45Peak','L56Trans',\
'SymLocal1','SymLocal2','SymLocal3','SymLocal4','SymLocal5','SymLocal6')
plotTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM', 'EXC', 'INH')
vmNames = []
scan_directory2(folder, vmNames, vmSuffix, contains)
synNames = []
scan_directory2(folder, synNames, suffix, contains)
nrOfFiles = len(synNames)
print 'Creating active synapse plots from %d files' % nrOfFiles
synData = {}
for fname in synNames:
activeSyns = scp.read_complete_synapse_activation_file(fname)
synData[fname] = activeSyns
synapseTimes = {}
spikeTrialSyns = {}
noSpikeTrialSyns = {}
for excType in excTypes:
synapseTimes[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
spikeTrialSyns[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
noSpikeTrialSyns[excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimes['EXC'] = {'ApicalDendrite': [], 'Dendrite': [], 'Total': []}
synapseTimes['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
spikeTrialSyns['EXC'] = {'ApicalDendrite': [], 'Dendrite': [], 'Total': []}
spikeTrialSyns['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
noSpikeTrialSyns['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
noSpikeTrialSyns['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
tStim = 253.0
earlyWindow = 17.0
trials = []
trialSpikeTimes = [[] for j in range(len(vmNames))]
trialWithSpikes = {}
for n in range(len(vmNames)):
fname = vmNames[n]
print 'Loading spike times from file %s' % fname
trialWithSpikes[n] = []
trialSpikeTimes_ = scp.read_spike_times_file(fname)
nrOfTrials = len(trialSpikeTimes_.keys())
trials.append(nrOfTrials)
for trial in trialSpikeTimes_.keys():
trialSpikeTimes[n].append([])
trialWithSpikes_ = False
for tSpike in trialSpikeTimes_[trial]:
if tSpike >= tStim and tSpike < tStim + earlyWindow:
trialSpikeTimes[n][-1].append(tSpike - tStim)
trialWithSpikes_ = True
trialWithSpikes[n].append(trialWithSpikes_)
for n in range(len(vmNames)):
nrSpikeTrials = 0
nrNoSpikeTrials = 0
earlyProxSyns = 0
for trialNr in range(trials[n]):
print 'Counting active synapses in trial %d of %d\r' % (
trialNr + 1, trials[n]),
sys.stdout.flush()
synTrialStr = 'simulation_run%04d_synapses.csv' % trialNr
synTrialFile = ''
tmpVmName = vmNames[n]
for name in synNames:
if synTrialStr in name and os.path.split(
tmpVmName)[0] == os.path.split(name)[0]:
synTrialFile = name
break
if synTrialFile == '':
errstr = 'Could not find synapse activation file for trial nr. %d' % trialNr
raise RuntimeError(errstr)
activeSyns = synData[synTrialFile]
for excType in excTypes:
synapseTimes[excType]['ApicalDendrite'].append([])
synapseTimes[excType]['Dendrite'].append([])
synapseTimes[excType]['Total'].append([])
if trialWithSpikes[n][trialNr]:
spikeTrialSyns[excType]['ApicalDendrite'].append([])
spikeTrialSyns[excType]['Dendrite'].append([])
spikeTrialSyns[excType]['Total'].append([])
else:
noSpikeTrialSyns[excType]['ApicalDendrite'].append([])
noSpikeTrialSyns[excType]['Dendrite'].append([])
noSpikeTrialSyns[excType]['Total'].append([])
synapseTimes['EXC']['ApicalDendrite'].append([])
synapseTimes['EXC']['Dendrite'].append([])
synapseTimes['EXC']['Total'].append([])
synapseTimes['INH']['ApicalDendrite'].append([])
synapseTimes['INH']['Dendrite'].append([])
synapseTimes['INH']['Soma'].append([])
synapseTimes['INH']['Total'].append([])
if trialWithSpikes[n][trialNr]:
spikeTrialSyns['EXC']['ApicalDendrite'].append([])
spikeTrialSyns['EXC']['Dendrite'].append([])
spikeTrialSyns['EXC']['Total'].append([])
spikeTrialSyns['INH']['ApicalDendrite'].append([])
spikeTrialSyns['INH']['Dendrite'].append([])
spikeTrialSyns['INH']['Soma'].append([])
spikeTrialSyns['INH']['Total'].append([])
else:
noSpikeTrialSyns['EXC']['ApicalDendrite'].append([])
noSpikeTrialSyns['EXC']['Dendrite'].append([])
noSpikeTrialSyns['EXC']['Total'].append([])
noSpikeTrialSyns['INH']['ApicalDendrite'].append([])
noSpikeTrialSyns['INH']['Dendrite'].append([])
noSpikeTrialSyns['INH']['Soma'].append([])
noSpikeTrialSyns['INH']['Total'].append([])
for synType in activeSyns.keys():
preCellType = synType.split('_')[0]
for excType in excTypes:
if excType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
if somaDist < 500.0:
synapseTimes[excType][structure][-1].extend(
synTimes)
synapseTimes[excType]['Total'][-1].extend(
synTimes)
synapseTimes['EXC'][structure][-1].extend(
synTimes)
synapseTimes['EXC']['Total'][-1].extend(
synTimes)
if trialWithSpikes[n][trialNr]:
spikeTrialSyns[excType][structure][
-1].extend(synTimes)
spikeTrialSyns[excType]['Total'][
-1].extend(synTimes)
spikeTrialSyns['EXC'][structure][
-1].extend(synTimes)
spikeTrialSyns['EXC']['Total'][-1].extend(
synTimes)
else:
noSpikeTrialSyns[excType][structure][
-1].extend(synTimes)
noSpikeTrialSyns[excType]['Total'][
-1].extend(synTimes)
noSpikeTrialSyns['EXC'][structure][
-1].extend(synTimes)
noSpikeTrialSyns['EXC']['Total'][
-1].extend(synTimes)
for inhType in inhTypes:
if inhType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
if somaDist < 500.0:
synapseTimes['INH'][structure][-1].extend(
synTimes)
synapseTimes['INH']['Total'][-1].extend(
synTimes)
if trialWithSpikes[n][trialNr]:
spikeTrialSyns['INH'][structure][
-1].extend(synTimes)
spikeTrialSyns['INH']['Total'][-1].extend(
synTimes)
else:
noSpikeTrialSyns['INH'][structure][
-1].extend(synTimes)
noSpikeTrialSyns['INH']['Total'][
-1].extend(synTimes)
print ''
tOffset = 100.0
tPlotBegin = 220.0
tPlotBeginWindow = 50.0
binWidth = 1.0
maxCount = 0
synapseHistograms = {}
spikeTrialHistograms = {}
noSpikeTrialHistograms = {}
for cellType in synapseTimes.keys():
synapseHistograms[cellType] = {}
spikeTrialHistograms[cellType] = {}
noSpikeTrialHistograms[cellType] = {}
for structure in synapseTimes[cellType].keys():
synTimes = synapseTimes[cellType][structure]
hist, bins = sca.PSTH_from_spike_times(
synTimes, binWidth, tOffset, tPlotBegin + tPlotBeginWindow)
synapseHistograms[cellType][structure] = hist, bins
spikeTrialSynTimes = spikeTrialSyns[cellType][structure]
hist2, bins2 = sca.PSTH_from_spike_times(
spikeTrialSynTimes, binWidth, tOffset,
tPlotBegin + tPlotBeginWindow)
spikeTrialHistograms[cellType][structure] = hist2, bins2
noSpikeTrialSynTimes = noSpikeTrialSyns[cellType][structure]
hist3, bins3 = sca.PSTH_from_spike_times(
noSpikeTrialSynTimes, binWidth, tOffset,
tPlotBegin + tPlotBeginWindow)
noSpikeTrialHistograms[cellType][structure] = hist3, bins3
tableOutName = outName + '_all_trials.csv'
with open(tableOutName, 'w') as outputTable:
hist, bins = synapseHistograms['EXC']['Total']
header = 'Bin start\tbin end\tbin center'
for cellType in plotTypes:
header += '\t'
header += cellType
header += '\n'
outputTable.write(header)
for i in range(len(bins) - 1):
line = str(bins[i])
line += '\t'
line += str(bins[i + 1])
line += '\t'
line += str(0.5 * (bins[i] + bins[i + 1]))
for cellType in plotTypes:
line += '\t'
line += str(synapseHistograms[cellType]['Total'][0][i])
line += '\n'
outputTable.write(line)
tableOutName2 = outName + '_spike_trials.csv'
with open(tableOutName2, 'w') as outputTable:
hist, bins = spikeTrialHistograms['EXC']['Total']
header = 'Bin start\tbin end\tbin center'
for cellType in plotTypes:
header += '\t'
header += cellType
header += '\n'
outputTable.write(header)
for i in range(len(bins) - 1):
line = str(bins[i])
line += '\t'
line += str(bins[i + 1])
line += '\t'
line += str(0.5 * (bins[i] + bins[i + 1]))
for cellType in plotTypes:
line += '\t'
line += str(spikeTrialHistograms[cellType]['Total'][0][i])
line += '\n'
outputTable.write(line)
tableOutName3 = outName + '_no_spike_trials.csv'
with open(tableOutName3, 'w') as outputTable:
hist, bins = noSpikeTrialHistograms['EXC']['Total']
header = 'Bin start\tbin end\tbin center'
for cellType in plotTypes:
header += '\t'
header += cellType
header += '\n'
outputTable.write(header)
for i in range(len(bins) - 1):
line = str(bins[i])
line += '\t'
line += str(bins[i + 1])
line += '\t'
line += str(0.5 * (bins[i] + bins[i + 1]))
for cellType in plotTypes:
line += '\t'
line += str(noSpikeTrialHistograms[cellType]['Total'][0][i])
line += '\n'
outputTable.write(line)
def create_active_synapse_histogram(folder, suffix, contains, outName):
'''
load all traces, compute spike times
and create raster plots
'''
excTypes = ('L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM')
inhTypes = ('L1','L23Trans','L45Sym','L45Peak','L56Trans',\
'SymLocal1','SymLocal2','SymLocal3','SymLocal4','SymLocal5','SymLocal6')
plotTypes = ('INH', 'L2', 'L34', 'L4py', 'L4sp', 'L4ss', 'L5st', 'L5tt',\
'L6cc', 'L6ccinv', 'L6ct', 'VPM')
columns = ('A1','A2','A3','A4','Alpha','B1','B2','B3','B4','Beta',\
'C1','C2','C3','C4','D1','D2','D3','D4','Delta','E1','E2','E3','E4','Gamma')
fnames = []
# scan_directory(folder, fnames, suffix)
scan_directory2(folder, fnames, suffix, contains)
nrOfFiles = len(fnames)
print 'Creating active synapse plots from %d files' % nrOfFiles
synapseTimes = {}
synapseTimesProximal = {}
synapseTimesDistal = {}
for col in columns:
synapseTimes[col] = {}
synapseTimesProximal[col] = {}
synapseTimesDistal[col] = {}
for excType in excTypes:
synapseTimes[col][excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesProximal[col][excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesDistal[col][excType] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimes[col]['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimes[col]['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
synapseTimesProximal[col]['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesProximal[col]['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
synapseTimesDistal[col]['EXC'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Total': []
}
synapseTimesDistal[col]['INH'] = {
'ApicalDendrite': [],
'Dendrite': [],
'Soma': [],
'Total': []
}
for n in range(len(fnames)):
fname = fnames[n]
print 'Loading synapse activation times from file %s (file %d of %d)\r' % (
fname, n + 1, nrOfFiles),
sys.stdout.flush()
activeSyns = scp.read_complete_synapse_activation_file(fname)
for col in columns:
for excType in excTypes:
synapseTimes[col][excType]['ApicalDendrite'].append([])
synapseTimes[col][excType]['Dendrite'].append([])
synapseTimes[col][excType]['Total'].append([])
synapseTimesProximal[col][excType]['ApicalDendrite'].append([])
synapseTimesProximal[col][excType]['Dendrite'].append([])
synapseTimesProximal[col][excType]['Total'].append([])
synapseTimesDistal[col][excType]['ApicalDendrite'].append([])
synapseTimesDistal[col][excType]['Dendrite'].append([])
synapseTimesDistal[col][excType]['Total'].append([])
synapseTimes[col]['EXC']['ApicalDendrite'].append([])
synapseTimes[col]['EXC']['Dendrite'].append([])
synapseTimes[col]['EXC']['Total'].append([])
synapseTimesProximal[col]['EXC']['ApicalDendrite'].append([])
synapseTimesProximal[col]['EXC']['Dendrite'].append([])
synapseTimesProximal[col]['EXC']['Total'].append([])
synapseTimesDistal[col]['EXC']['ApicalDendrite'].append([])
synapseTimesDistal[col]['EXC']['Dendrite'].append([])
synapseTimesDistal[col]['EXC']['Total'].append([])
synapseTimes[col]['INH']['ApicalDendrite'].append([])
synapseTimes[col]['INH']['Dendrite'].append([])
synapseTimes[col]['INH']['Soma'].append([])
synapseTimes[col]['INH']['Total'].append([])
synapseTimesProximal[col]['INH']['ApicalDendrite'].append([])
synapseTimesProximal[col]['INH']['Dendrite'].append([])
synapseTimesProximal[col]['INH']['Soma'].append([])
synapseTimesProximal[col]['INH']['Total'].append([])
synapseTimesDistal[col]['INH']['ApicalDendrite'].append([])
synapseTimesDistal[col]['INH']['Dendrite'].append([])
synapseTimesDistal[col]['INH']['Soma'].append([])
synapseTimesDistal[col]['INH']['Total'].append([])
for synType in activeSyns.keys():
preCellType = synType.split('_')[0]
preCol = synType.split('_')[1]
for col in columns:
if col == preCol:
for excType in excTypes:
if excType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
synapseTimes[col][excType][structure][
n].extend(synTimes)
synapseTimes[col][excType]['Total'][n].extend(
synTimes)
synapseTimes[col]['EXC'][structure][n].extend(
synTimes)
synapseTimes[col]['EXC']['Total'][n].extend(
synTimes)
if somaDist < 500.0:
synapseTimesProximal[col][excType][
structure][n].extend(synTimes)
synapseTimesProximal[col][excType][
'Total'][n].extend(synTimes)
synapseTimesProximal[col]['EXC'][
structure][n].extend(synTimes)
synapseTimesProximal[col]['EXC']['Total'][
n].extend(synTimes)
if somaDist >= 500.0:
synapseTimesDistal[col][excType][
structure][n].extend(synTimes)
synapseTimesDistal[col][excType]['Total'][
n].extend(synTimes)
synapseTimesDistal[col]['EXC'][structure][
n].extend(synTimes)
synapseTimesDistal[col]['EXC']['Total'][
n].extend(synTimes)
for inhType in inhTypes:
if inhType == preCellType:
for syn in activeSyns[synType]:
somaDist = syn[1]
structure = syn[4]
synTimes = syn[5]
synapseTimes[col]['INH'][structure][n].extend(
synTimes)
synapseTimes[col]['INH']['Total'][n].extend(
synTimes)
if somaDist < 500.0:
synapseTimesProximal[col]['INH'][
structure][n].extend(synTimes)
synapseTimesProximal[col]['INH']['Total'][
n].extend(synTimes)
if somaDist >= 500.0:
synapseTimesDistal[col]['INH'][structure][
n].extend(synTimes)
synapseTimesDistal[col]['INH']['Total'][
n].extend(synTimes)
print ''
tOffset = 100.0
tOngoing = 120.0
tOngoingWindow = 100.0
tStim = 245.0
tStimWindow = 25.0
binWidth = 1.0
maxCount = 0
synapseHistogramsEvoked = {}
synapseHistogramsOngoing = {}
synapseHistogramsEvokedProximal = {}
synapseHistogramsOngoingProximal = {}
synapseHistogramsEvokedDistal = {}
synapseHistogramsOngoingDistal = {}
for col in columns:
synapseHistogramsEvoked[col] = {}
synapseHistogramsOngoing[col] = {}
synapseHistogramsEvokedProximal[col] = {}
synapseHistogramsOngoingProximal[col] = {}
synapseHistogramsEvokedDistal[col] = {}
synapseHistogramsOngoingDistal[col] = {}
for cellType in synapseTimes[col].keys():
synapseHistogramsEvoked[col][cellType] = {}
synapseHistogramsOngoing[col][cellType] = {}
synapseHistogramsEvokedProximal[col][cellType] = {}
synapseHistogramsOngoingProximal[col][cellType] = {}
synapseHistogramsEvokedDistal[col][cellType] = {}
synapseHistogramsOngoingDistal[col][cellType] = {}
for structure in synapseTimes[col][cellType].keys():
synTimes1 = synapseTimes[col][cellType][structure]
hist1, bins1 = sca.PSTH_from_spike_times(
synTimes1, binWidth, tStim, tStim + tStimWindow)
synapseHistogramsEvoked[col][cellType][structure] = np.sum(
hist1)
hist2, bins2 = sca.PSTH_from_spike_times(
synTimes1, binWidth, tOngoing, tOngoing + tOngoingWindow)
synapseHistogramsOngoing[col][cellType][structure] = np.sum(
hist2)
synTimes2 = synapseTimesProximal[col][cellType][structure]
hist3, bins3 = sca.PSTH_from_spike_times(
synTimes2, binWidth, tStim, tStim + tStimWindow)
synapseHistogramsEvokedProximal[col][cellType][
structure] = np.sum(hist3)
hist4, bins4 = sca.PSTH_from_spike_times(
synTimes2, binWidth, tOngoing, tOngoing + tOngoingWindow)
synapseHistogramsOngoingProximal[col][cellType][
structure] = np.sum(hist4)
synTimes3 = synapseTimesDistal[col][cellType][structure]
hist5, bins5 = sca.PSTH_from_spike_times(
synTimes3, binWidth, tStim, tStim + tStimWindow)
synapseHistogramsEvokedDistal[col][cellType][
structure] = np.sum(hist5)
hist6, bins6 = sca.PSTH_from_spike_times(
synTimes3, binWidth, tOngoing, tOngoing + tOngoingWindow)
synapseHistogramsOngoingDistal[col][cellType][
structure] = np.sum(hist6)
ongoingOutName = outName + '_ongoing_syn.csv'
with open(ongoingOutName, 'w') as outputTable:
header = 'Column\tcell type\tnr. of active synapses (100ms)\n'
outputTable.write(header)
for cellType in plotTypes:
for col in columns:
line = col
line += '\t'
line += cellType
line += '\t'
line += str(synapseHistogramsOngoing[col][cellType]['Total'])
line += '\n'
outputTable.write(line)
writeHeader = True
evokedOutName = outName + '_total_evoked_syn.csv'
with open(evokedOutName, 'w') as outputTable:
header = 'Column\tcell type\tnr. of active synapses (25ms post-stimulus)\n'
outputTable.write(header)
for cellType in plotTypes:
for col in columns:
line = col
line += '\t'
line += cellType
line += '\t'
line += str(synapseHistogramsEvoked[col][cellType]['Total'])
line += '\n'
outputTable.write(line)
ongoingProximalOutName = outName + '_ongoing_proximal_syn.csv'
with open(ongoingProximalOutName, 'w') as outputTable:
header = 'Column\tcell type\tnr. of active synapses (100ms)\n'
outputTable.write(header)
for cellType in plotTypes:
for col in columns:
line = col
line += '\t'
line += cellType
line += '\t'
line += str(
synapseHistogramsOngoingProximal[col][cellType]['Total'])
line += '\n'
outputTable.write(line)
writeHeader = True
evokedProximalOutName = outName + '_total_evoked_proximal_syn.csv'
with open(evokedProximalOutName, 'w') as outputTable:
header = 'Column\tcell type\tnr. of active synapses (25ms post-stimulus)\n'
outputTable.write(header)
for cellType in plotTypes:
for col in columns:
line = col
line += '\t'
line += cellType
line += '\t'
line += str(
synapseHistogramsEvokedProximal[col][cellType]['Total'])
line += '\n'
outputTable.write(line)
ongoingDistalOutName = outName + '_ongoing_distal_syn.csv'
with open(ongoingDistalOutName, 'w') as outputTable:
header = 'Column\tcell type\tnr. of active synapses (100ms)\n'
outputTable.write(header)
for cellType in plotTypes:
for col in columns:
line = col
line += '\t'
line += cellType
line += '\t'
line += str(
synapseHistogramsOngoingDistal[col][cellType]['Total'])
line += '\n'
outputTable.write(line)
writeHeader = True
evokedDistalOutName = outName + '_total_evoked_distal_syn.csv'
with open(evokedDistalOutName, 'w') as outputTable:
header = 'Column\tcell type\tnr. of active synapses (25ms post-stimulus)\n'
outputTable.write(header)
for cellType in plotTypes:
for col in columns:
line = col
line += '\t'
line += cellType
line += '\t'
line += str(
synapseHistogramsEvokedDistal[col][cellType]['Total'])
line += '\n'
outputTable.write(line)
def estimate_PSTH_convergence_rate(folder, suffix, contains):
'''
load all traces, compute spike times and PSTH
and determine empirical rate of convergence
'''
fnames = []
scan_directory2(folder, fnames, suffix, contains)
print 'Creating spike raster plots from %d files' % len(fnames)
tOffset = 100.0
tStop = 345.0
tOngoing = 120.0
tOngoingWindow = 100.0
tStim = 245.0
tStimWindow = 50.0
binSize = 5.0
ongoingBeginBin = int((tOngoing - tOffset) / binSize + 0.5)
ongoingEndBin = int((tOngoing + tOngoingWindow - tOffset) / binSize + 0.5)
stimBeginBin = int((tStim - tOffset) / binSize + 0.5)
stimEndBin = int((tStim + tStimWindow - tOffset) / binSize + 0.5)
allOngoingRMSE = []
allPSTH_RMSE = []
for n in range(len(fnames)):
fname = fnames[n]
print 'Loading traces from file %s' % fname
data = np.loadtxt(fname, skiprows=1, unpack=True)
t = data[0]
allSpikeTimes = []
cumulativePSTHs = []
for i in range(1, len(data)):
v = data[i]
spikeTimes = sca.simple_spike_detection(t, v)
allSpikeTimes.append(spikeTimes)
# PSTH of all traces so far
cumulativePSTHs.append(
sca.PSTH_from_spike_times(allSpikeTimes, binSize, tOffset,
tStop))
hist, bins = sca.PSTH_from_spike_times(allSpikeTimes, binSize, tOffset,
tStop)
refOngoing = np.mean(hist[ongoingBeginBin:ongoingEndBin])
refPSTH = np.array(hist[stimBeginBin:stimEndBin]) - refOngoing
ongoingRMSE = []
PSTH_RMSE = []
for PSTH in cumulativePSTHs:
hist, bins = PSTH
currentOngoing = np.mean(hist[ongoingBeginBin:ongoingEndBin])
currentPSTH = np.array(
hist[stimBeginBin:stimEndBin]) - currentOngoing
ongoingRMSE.append(
np.abs(currentOngoing - refOngoing) / refOngoing)
PSTH_RMSE.append(
np.sqrt(np.dot(currentPSTH - refPSTH, currentPSTH - refPSTH)) /
np.max(refPSTH))
allOngoingRMSE.append(ongoingRMSE)
allPSTH_RMSE.append(PSTH_RMSE)
avgOngoingRMSE = np.mean(np.array(allOngoingRMSE), axis=0)
stdOngoingRMSE = np.std(np.array(allOngoingRMSE), axis=0)
avgPSTH_RMSE = np.mean(np.array(allPSTH_RMSE), axis=0)
stdPSTH_RMSE = np.mean(np.array(allPSTH_RMSE), axis=0)
trials = [i + 1 for i in range(len(avgOngoingRMSE))]
fig = plt.figure(1)
fig.add_subplot(2, 1, 1)
plt.plot(trials, avgOngoingRMSE, 'k')
plt.plot(trials, avgOngoingRMSE + stdOngoingRMSE, 'k--')
plt.plot(trials, avgOngoingRMSE - stdOngoingRMSE, 'k--')
plt.ylabel('ongoing RMSE')
plt.ylim([0, np.max(avgOngoingRMSE + stdOngoingRMSE)])
fig.add_subplot(2, 1, 2)
plt.plot(trials, avgPSTH_RMSE, 'k')
plt.plot(trials, avgPSTH_RMSE + stdPSTH_RMSE, 'k--')
plt.plot(trials, avgPSTH_RMSE - stdPSTH_RMSE, 'k--')
plt.ylim([0, np.max(avgPSTH_RMSE + stdPSTH_RMSE)])
plt.xlabel('nr. of trials')
plt.ylabel('PSTH RMSE')
plt.show()
def create_spike_raster_plots(folder, suffix):
'''
load all traces, compute spike times
and create raster plots
'''
fnames = []
scan_directory(folder, fnames, suffix)
print 'Creating spike raster plots from %d files' % len(fnames)
tOffset = 100.0
tStop = 345.0
for n in range(len(fnames)):
fname = fnames[n]
print 'Loading traces from file %s' % fname
data = np.loadtxt(fname, skiprows=1, unpack=True)
t = data[0]
allSpikeTimes = []
ax = []
fig = plt.figure(2 * n + 1)
for i in range(1, len(data)):
fig.add_subplot(2, 1, 1)
ax.append(plt.plot(t, data[i]))
fig.add_subplot(2, 1, 2)
v = data[i]
spikeTimes = sca.simple_spike_detection(t, v)
allSpikeTimes.append(spikeTimes)
spikes = [i for time in spikeTimes]
ax.append(plt.plot(spikeTimes, spikes, 'k|'))
# plt.plot(spikeTimes, spikes, 'k|')
fig.add_subplot(2, 1, 1)
plt.xlabel('t [ms]')
plt.ylabel('Vm [mV]')
plt.xlim([tOffset, tStop])
fig.add_subplot(2, 1, 2)
plt.xlabel('t [ms]')
plt.ylabel('trial nr.')
plt.xlim([tOffset, tStop])
outName = fname[:-4]
outName += '_spike_raster_plot.pdf'
plt.savefig(outName)
# hist, bins = sca.PSTH_from_spike_times(allSpikeTimes, 1.0, 200.0, 250.0)
# hist, bins = sca.PSTH_from_spike_times(allSpikeTimes, 1.0, 100.0, 250.0)
# hist, bins = sca.PSTH_from_spike_times(allSpikeTimes, 5.0, 100.0, 350.0)
hist, bins = sca.PSTH_from_spike_times(allSpikeTimes, 5.0, tOffset,
tStop)
# hist, bins = sca.PSTH_from_spike_times(allSpikeTimes, 10.0, 100.0, 250.0)
offset = 0.5 * (bins[1] - bins[0])
fig = plt.figure(2 * n + 2)
# plt.bar(bins[:-1]+offset, hist, color='b')
plt.bar(bins[:-1], hist, color='b', width=5)
# plt.bar(bins[:-1], hist, color='b', width=10)
plt.xlabel('t [ms]')
plt.ylabel('AP/stim')
plt.xlim([tOffset, tStop])
outName = fname[:-4]
# outName += '_PSTH_total_10ms.pdf'
outName += '_PSTH_total_5ms.pdf'
plt.savefig(outName)
scp.write_PSTH(outName[:-4] + '.csv', hist, bins)