def get_spike_properties_in_response_to_conductance_peak(
dirname, spike_threshold, Gleak):
"""
Calculate spike width and inter-spike intervals for all files with responses to
excitatory conductance kick
"""
files = os.listdir(dirname)
Gkick = []
spike_amplitudes = []
spike_widths = []
spike_peaks = []
spike_dips = []
interspike_intervals = []
for f in files:
if ("RA" in f) and (not "_dend_" in f) and (not "_soma_" in f):
print f
t, Vs, Vd, Gexc_d, Ginh_d, n,\
h, r, c, Ca = reading.read_hh2_buffer_full(os.path.join(dirname, f))
spike_amplitude, spike_peak, spike_dip, spike_width, interspike_interval = calculate_spike_properties(
t, Vs, spike_threshold)
Gkick.append(np.max(Gexc_d) / Gleak)
spike_amplitudes.append(spike_amplitude)
spike_widths.append(spike_width)
spike_peaks.append(spike_peak)
spike_dips.append(spike_dip)
interspike_intervals.append(interspike_interval)
Gkick, spike_amplitudes, spike_peaks, spike_dips, spike_widths, interspike_intervals = zip(
*sorted(
zip(Gkick, spike_amplitudes, spike_peaks, spike_dips, spike_widths,
interspike_intervals)))
return Gkick, spike_amplitudes, spike_peaks, spike_dips, spike_widths, interspike_intervals
#filename = "/home/eugene/Output/neuronTest/saturatedInhibitionResponse/Ginh_5.00.bin"
#filename = "/home/eugene/Output/neuronTest/kickResponse/Ginh_10.00_8.bin"
#filename = "/home/eugene/Output/neuronTest/response.bin"
#filename = "/home/eugene/Output/networks/chainGrowth/passiveDendrite/maturationTransition1/RA/RA570.bin"
#filename = "/home/eugene/Output/neuronTest/noise/noise_immature_4.bin"
#filename = "/mnt/hodgkin/eugene/Output/tuneHVCRA/finalModel/KLTH3.5Rest55mVExamples/age0/RA99.bin"
filename = "/mnt/hodgkin/eugene/Output/tuneHVCRA/finalModel/KLTH3.5Rest55mVExamples/age1.0/RA45.bin"
#filename = "/mnt/hodgkin/eugene/Output/tuneHVCRA/finalModel/KLTH/age100/RA7.bin"
#filename = "/mnt/hodgkin/eugene/Output/tuneHVCRA/finalModel/noKLTHtest/age100/RA40.bin"
#filename = "/mnt/hodgkin/eugene/Output/tuneHVCRA/finalModel/noKLTHCalciumSpike20msLeak0.75/age100/RA10.bin"
t, Vs, Vd, Gexc_d, Ginh_d, n, h, r, c, Ca = reading.read_hh2_buffer_full(
filename)
#print t
#print Vs
print Vs[-1]
print Vd[-1]
print n[-1]
print h[-1]
print r[-1]
print c[-1]
print Ca[-1]
print "Average Vs = ", np.mean(Vs)
print "Std Vs = ", np.std(Vs)
Gkick_tuned3, spike_amplitude_tuned3, spike_peaks_tuned3, spike_dips_tuned3, spike_width_tuned3, interspike_interval_tuned3 = \
get_spike_properties_in_response_to_conductance_peak(dirname_tuned3, spike_threshold, Gleak_tuned3)
Gkick_tuned4, spike_amplitude_tuned4, spike_peaks_tuned4, spike_dips_tuned4, spike_width_tuned4, interspike_interval_tuned4 = \
get_spike_properties_in_response_to_conductance_peak(dirname_tuned4, spike_threshold, Gleak_tuned4)
# compare neuron traces
file_original_trace = os.path.join(dirname_original, "RA35.bin")
file_tuned_trace = os.path.join(dirname_tuned, "RA35.bin")
file_tuned_trace2 = os.path.join(dirname_tuned2, "RA35.bin")
file_tuned_trace3 = os.path.join(dirname_tuned3, "RA35.bin")
file_tuned_trace4 = os.path.join(dirname_tuned4, "RA35.bin")
t_original, Vs_original, Vd_original, Gexc_d_original, Ginh_d_original, n_original,\
h_original, r_original, c_original, Ca_original = reading.read_hh2_buffer_full(file_original_trace)
t_tuned, Vs_tuned, Vd_tuned, Gexc_d_tuned, Ginh_d_tuned, n_tuned,\
h_tuned, r_tuned, c_tuned, Ca_tuned = reading.read_hh2_buffer_full(file_tuned_trace)
t_tuned2, Vs_tuned2, Vd_tuned2, Gexc_d_tuned2, Ginh_d_tuned2, n_tuned2,\
h_tuned2, r_tuned2, c_tuned2, Ca_tuned2 = reading.read_hh2_buffer_full(file_tuned_trace2)
t_tuned3, Vs_tuned3, Vd_tuned3, Gexc_d_tuned3, Ginh_d_tuned3, n_tuned3,\
h_tuned3, r_tuned3, c_tuned3, Ca_tuned3 = reading.read_hh2_buffer_full(file_tuned_trace3)
t_tuned4, Vs_tuned4, Vd_tuned4, Gexc_d_tuned4, Ginh_d_tuned4, n_tuned4,\
h_tuned4, r_tuned4, c_tuned4, Ca_tuned4 = reading.read_hh2_buffer_full(file_tuned_trace4)
#print calculate_spike_properties(t_original, Vs_original, -20.0)
#print calculate_spike_properties(t_tuned, Vs_tuned, -20.0)
import numpy as np #filename = "/home/eugene/Output/neuronTest/saturatedInhibitionResponse/Ginh_5.00.bin" #filename = "/home/eugene/Output/neuronTest/kickResponse/Ginh_10.00_8.bin" #filename = "/home/eugene/Output/neuronTest/response.bin" #filename = "/home/eugene/Output/networks/chainGrowth/passiveDendrite/test1/RA/RA231.bin" #filename = "/home/eugene/Output/networks/chainGrowth/testGrowthDelays5/RA/RA5.bin" #filename = "/home/eugene/Output/neuronTest/inhAndExcInputsResponse/RA.bin" #filename = "/home/eugene/Output/neuronTest/modelStability/RA29.bin" filename1 = "/mnt/hodgkin_home/eugene/Output/tuneHVCRA/Ad1000/Rc200/RA30.bin" filename2 = "/mnt/hodgkin_home/eugene/Output/tuneHVCRA/Ad1000/Rc200/RA25.bin" #filename2 = "/mnt/hodgkin_home/eugene/Output/tuneHVCRA/passiveDendrite/NaKchange/Na80K8/RA23.bin" #filename = "/mnt/hodgkin_home/eugene/Output/neuronTest/inhAndExcInputsResponse/RA13.bin" t1, Vs1, Vd1, Gexc_d1, Ginh_d1, n1, h1, r1, c1, Ca1 = reading.read_hh2_buffer_full(filename1) t2, Vs2, Vd2, Gexc_d2, Ginh_d2, n2, h2, r2, c2, Ca2 = reading.read_hh2_buffer_full(filename2) #print t #print Vs label1 = 'Rc200 30 Ge1.05' label2 = 'Rc200 25 Ge1.5' tmin = 40 tmax = 100 # membrane potentials f = plt.figure()
import numpy as np
dirname = "/home/eugene/Output/networks/chainGrowth/matureTest/RA"
fileMature = "/home/eugene/Output/networks/chainGrowth/testGrowthDelays3/mature_5300.bin"
files = os.listdir(dirname)
(_, _, mature_indicators) = reading.read_mature_indicators(fileMature)
mature_neurons = set(np.where(mature_indicators == 1)[0])
counter = 0
counter_mature = 0
for f in files:
t, Vs, Vd, Gexc_d, Ginh_d, n, h, r, c, Ca = reading.read_hh2_buffer_full(
os.path.join(dirname, f))
if counter == 0:
Ginh = np.copy(Ginh_d)
else:
Ginh += Ginh_d
neuron_id = int(f.split(".bin")[0][2:])
print neuron_id
if neuron_id in mature_neurons:
if counter_mature == 0:
Ginh_mature = np.copy(Ginh_d)
else:
Ginh_mature += Ginh_d
def compare_networkAndPoolConductance(dataDir, testDataDir, outFigureDir,
simName, trial):
"""
Function reads conductances for all neurons in the network, calculates average total conductances
and conductances alinged to average bursting times
"""
N_RA, N_I = reading.read_num_neurons(
os.path.join(dataDir, "num_neurons.bin"))
training_neurons = reading.read_training_neurons(
os.path.join(dataDir, "training_neurons.bin"))
N_TR = len(training_neurons)
_, numTestTrials, \
probability_soma_spike, average_num_soma_spikes_in_trial, mean_first_soma_spike_time, std_first_soma_spike_time,\
probability_dend_spike, average_num_dend_spikes_in_trial, mean_first_dend_spike_time, std_first_dend_spike_time = reading.read_jitter(os.path.join(testDataDir, "jitter.bin"))
neuronsWithRobustDendriticSpike = np.where(
probability_dend_spike >= 0.75)[0]
meanFirstSomaSpikeOfNeuronsWithRoburstDendriticSpike = mean_first_soma_spike_time[
neuronsWithRobustDendriticSpike]
t, Vs, Vd, Gexc_d, Ginh_d, n, h, r, c, Ca = reading.read_hh2_buffer_full(
os.path.join(testDataDir, "RA/RA0_trial0.bin"))
Ginh = np.zeros((N_RA, len(t)), np.float32)
Gexc = np.zeros((N_RA, len(t)), np.float32)
numOtherNeurons = N_RA - 1
GinhSumAll = np.zeros(len(t), np.float32)
GexcSumAll = np.zeros(len(t), np.float32)
for testTrial in range(numTestTrials):
print "Test trial: ", testTrial
#if testTrial == 1:
# break
for neuronId in range(N_RA):
t, Vs, Vd, Gexc_d, Ginh_d, n, h, r, c, Ca = reading.read_hh2_buffer_full(
os.path.join(
testDataDir, "RA/RA" + str(neuronId) + "_trial" +
str(testTrial) + ".bin"))
Ginh[neuronId] += Ginh_d
Gexc[neuronId] += Gexc_d
# sum of conductances for all neurons excluding training
if neuronId not in training_neurons:
GinhSumAll += Ginh_d
GexcSumAll += Gexc_d
for neuronId in range(N_RA):
Ginh[neuronId] /= float(numTestTrials)
GinhSumAll = GinhSumAll / (float(numTestTrials) * float(N_RA - N_TR))
GexcSumAll = GexcSumAll / (float(numTestTrials) * float(N_RA - N_TR))
#print np.max(spike_times_d)
#print np.max(t)
#window = 100.0 # window size in ms
window = 50.0
Gbursted = None # conductance aligned to bursting time
Gother = None # conductance of neurons that did npt burst
dt = t[1] - t[0]
window_t = [
float(i) * dt - window / 2. for i in range(int(window / dt) - 1)
]
GburstedInh_window = np.empty(
(len(neuronsWithRobustDendriticSpike) - N_TR, int(window / dt) - 1),
np.float32) # conductances of all burst neurons aligned to burst time
GburstedExc_window = np.empty(
(len(neuronsWithRobustDendriticSpike) - N_TR, int(window / dt) - 1),
np.float32) # conductances of all burst neurons aligned to burst time
# plot conductances for several random bursted neurons
np.random.seed(1991)
nid_toPlot = np.random.choice(neuronsWithRobustDendriticSpike,
16,
replace=False)
nrows = 4
ncols = 4
fInh, axarrInh = plt.subplots(nrows=nrows, ncols=ncols)
fExc, axarrExc = plt.subplots(nrows=nrows, ncols=ncols)
neuronPlotCounter = 0
neuronSavedCounter = 0
for nid, meanFirstSpikeTime in zip(
neuronsWithRobustDendriticSpike,
meanFirstSomaSpikeOfNeuronsWithRoburstDendriticSpike):
if nid in training_neurons:
continue
meanFirstSpikeTime = round(int(meanFirstSpikeTime / dt) * dt, 2)
GburstedInh_window[neuronSavedCounter] = Ginh[nid][
(t > meanFirstSpikeTime - window / 2.)
& (t < meanFirstSpikeTime + window / 2.)]
GburstedExc_window[neuronSavedCounter] = Gexc[nid][
(t > meanFirstSpikeTime - window / 2.)
& (t < meanFirstSpikeTime + window / 2.)]
# normalize conductance by max value
#Gbursted_window[neuronSavedCounter] /= np.max(Gbursted_window[neuronSavedCounter])
# normalize to o mean and unit variance
#Gbursted_window[neuronSavedCounter] = sklearn.preprocessing.scale(Gbursted_window[neuronSavedCounter], axis=0, with_mean=True, with_std=True, copy=True)
if nid in nid_toPlot:
row = neuronPlotCounter // 4
col = neuronPlotCounter % 4
axarrInh[row, col].plot(window_t,
GburstedInh_window[neuronSavedCounter])
axarrExc[row, col].plot(window_t,
GburstedExc_window[neuronSavedCounter])
#axarr[row, col].vlines(meanFirstSpikeTime, 0, np.max(Ginh[nid]))
if row == 3:
axarrInh[row, col].set_xlabel('Time (ms)')
axarrExc[row, col].set_xlabel('Time (ms)')
if col == 0:
axarrInh[row, col].set_ylabel('Ginh (mS/cm^2)')
axarrExc[row, col].set_ylabel('Gexc (mS/cm^2)')
neuronPlotCounter += 1
neuronSavedCounter += 1
#if Gbursted == None:
# Gbursted = Ginh[nid[0]][(t > dend_spike_time[0] - window/2.)&(t < dend_spike_time[0] + window/2.)]
#else:
# Gbursted += Ginh[nid[0]][(t > dend_spike_time[0] - window/2.)&(t < dend_spike_time[0] + window/2.)]
#if Gother == None:
# Gother = (GsumAll-Ginh[nid[0]])[(t > dend_spike_time[0] - window/2.)&(t < dend_spike_time[0] + window/2.)]
#else:
# Gother += (GsumAll-Ginh[nid[0]])[(t > dend_spike_time[0] - window/2.)&(t < dend_spike_time[0] + window/2.)]
#plt.figure()
#plt.plot(t, Gexc_d)
#plt.vlines(dend_spike_time[0], 0, np.max(Gexc_d))
#plt.figure()
#plt.plot(t, G)
#plt.vlines(dend_spike_time[0], 0, np.max(G))
w, h = maximize_figure(fInh.number)
fInh.savefig(outFigureDir + simName + "_trial" + str(trial) +
'_Ginh_examples.png',
bbox_inches='tight')
plt.close(fInh)
w, h = maximize_figure(fExc.number)
fExc.savefig(outFigureDir + simName + "_trial" + str(trial) +
'_Gexc_examples.png',
bbox_inches='tight')
plt.close(fExc)
GburstedInh = np.sum(
GburstedInh_window,
axis=0) / float(len(neuronsWithRobustDendriticSpike) - N_TR)
std_GburstedInh = np.std(GburstedInh_window, axis=0)
GburstedExc = np.sum(
GburstedExc_window,
axis=0) / float(len(neuronsWithRobustDendriticSpike) - N_TR)
std_GburstedExc = np.std(GburstedExc_window, axis=0)
f = plt.figure()
plt.plot(window_t, GburstedInh, label='bursted neurons')
plt.plot(
window_t, GburstedInh +
std_GburstedInh / np.sqrt(len(neuronsWithRobustDendriticSpike) - N_TR))
plt.plot(
window_t, GburstedInh -
std_GburstedInh / np.sqrt(len(neuronsWithRobustDendriticSpike) - N_TR))
plt.xlabel('Time (ms)')
plt.ylabel('average Ginh (mS/cm^2)')
f.savefig(outFigureDir + simName + "_trial" + str(trial) +
'_average_Ginh.png',
bbox_inches='tight')
plt.close(f)
#plt.plot(window_t, Gother / float((N_RA - N_TR - 1)*numNeuronsWithDendSpike), label='other neurons')
#plt.legend()
f = plt.figure()
plt.plot(t, GinhSumAll)
plt.xlabel('Time (ms)')
plt.ylabel('total Ginh (mS/cm^2)')
f.savefig(outFigureDir + simName + "_trial" + str(trial) +
'_total_Ginh.png',
bbox_inches='tight')
plt.close(f)
f = plt.figure()
plt.plot(window_t, GburstedExc, label='bursted neurons')
plt.plot(
window_t, GburstedExc +
std_GburstedExc / np.sqrt(len(neuronsWithRobustDendriticSpike) - N_TR))
plt.plot(
window_t, GburstedExc -
std_GburstedExc / np.sqrt(len(neuronsWithRobustDendriticSpike) - N_TR))
plt.xlabel('Time (ms)')
plt.ylabel('average Gexc (mS/cm^2)')
f.savefig(outFigureDir + simName + "_trial" + str(trial) +
'_average_Gexc.png',
bbox_inches='tight')
plt.close(f)
#plt.plot(window_t, Gother / float((N_RA - N_TR - 1)*numNeuronsWithDendSpike), label='other neurons')
#plt.legend()
f = plt.figure()
plt.plot(t, GexcSumAll)
plt.xlabel('Time (ms)')
plt.ylabel('total Gexc (mS/cm^2)')
f.savefig(outFigureDir + simName + "_trial" + str(trial) +
'_total_Gexc.png',
bbox_inches='tight')
plt.close(f)