def get_num_active_and_super_synapses(dirname, end_trial, trialStep):
"""
Calculate time sequence of total number of active and super connections in the network
"""
num_timepoints = end_trial / trialStep + 1
num_active = np.zeros(num_timepoints, np.int32)
num_super = np.zeros(num_timepoints, np.int32)
current_trial = 0
timepoint = 0
while current_trial <= end_trial:
fileActive = os.path.join(dirname, "RA_RA_active_connections_" + str(current_trial) + ".bin")
fileSuper = os.path.join(dirname, "RA_RA_super_connections_" + str(current_trial) + ".bin")
(_, _, active_synapses) = reading.read_synapses(fileActive)
(_, _, super_synapses) = reading.read_synapses(fileSuper)
for i in range(len(active_synapses)):
num_active[timepoint] += len(active_synapses[i])
num_super[timepoint] += len(super_synapses[i])
timepoint += 1
current_trial += trialStep
return num_active, num_super
def get_total_input_weight_time_sequence(dirname, end_trial, trialStep, target_neurons):
"""
Outputs total excitatory conductance input to target_neurons at different
points during simulation
"""
num_timepoints = end_trial / trialStep + 1
input_weights = np.zeros((len(target_neurons), num_timepoints), np.float32)
current_trial = 0
timepoint = 0
while current_trial <= end_trial:
fileWeights = os.path.join(dirname, "weights_" + str(current_trial) + ".bin")
fileActive = os.path.join(dirname, "RA_RA_active_connections_" + str(current_trial) + ".bin")
(N, _, weights) = reading.read_weights(fileWeights)
(_, _, active_synapses) = reading.read_synapses(fileActive)
for source_id in range(N):
for i, target_id in enumerate(target_neurons):
if target_id in active_synapses[source_id]:
input_weights[i][timepoint] += weights[source_id][target_id]
timepoint += 1
current_trial += trialStep
return input_weights
def get_networkSize_vs_time(dirname):
"""
Get network size and time from files in the directory
"""
files = os.listdir(dirname)
time = []
network_size = []
for f in files:
if "RA_RA_super_connections" in f:
time.append(int(f.split("_")[-1].split(".bin")[0]))
(N_RA, _, super_synapses) = reading.read_synapses(os.path.join(dirname, f))
has_input_supersynapse = np.zeros(N_RA, np.bool)
#print has_input_supersynapse
for i in range(N_RA):
for target in super_synapses[i]:
has_input_supersynapse[target] = True
network_size.append(len(np.where(has_input_supersynapse == True)[0]))
return time, network_size
def write_pajek_neurons_connected_by_supersynapses(dirname, trial_number):
"""
Create .net file with locations and supersynaptic connections for HVC-RA neurons connected by supersynapses
"""
file_RA_xy = os.path.join(dirname, "RA_xy_" + str(trial_number) + ".bin")
file_training = os.path.join(dirname, "training_neurons.bin")
file_pajek = os.path.join(dirname, "network_" + str(trial_number) + ".net")
fileSuperSynapses = os.path.join(
dirname, "RA_RA_super_connections_" + str(trial_number) + ".bin")
fileWeights = os.path.join(dirname,
"weights_" + str(trial_number) + ".bin")
coord_RA = reading.read_coordinates(file_RA_xy)
training_neurons = reading.read_training_neurons(file_training)
(N_RA, _, super_synapses) = reading.read_synapses(fileSuperSynapses)
(N_RA, _, weights) = reading.read_weights(fileWeights)
network_neurons = set(training_neurons)
for i in range(N_RA):
for target in super_synapses[i]:
network_neurons.add(target)
network_neurons = sorted(list(network_neurons))
num_neurons = len(network_neurons)
# sort array with neurons and training neurons #
training_neurons.sort()
with open(file_pajek, 'w') as f:
f.write("*Vertices {0}\n".format(num_neurons))
for i, neuron_id in enumerate(network_neurons):
if neuron_id in training_neurons:
f.write('{0} "{1}" {2} {3} {4} ic Green\n'.format(
i + 1, neuron_id, coord_RA[neuron_id][0],
coord_RA[neuron_id][1], coord_RA[neuron_id][2]))
else:
f.write('{0} "{1}" {2} {3} {4} ic Yellow\n'.format(
i + 1, neuron_id, coord_RA[neuron_id][0],
coord_RA[neuron_id][1], coord_RA[neuron_id][2]))
f.write("*Arcs\n")
# write targets of HVC(RA) neurons
for i, source_id in enumerate(network_neurons):
for target_id in super_synapses[source_id]:
try:
ind = utils.index(network_neurons, target_id)
f.write('{0} {1} {2} c Green\n'.format(
i + 1, ind + 1, weights[source_id][target_id]))
except ValueError:
continue
def write_pajek_neurons(dirname, trial_number):
"""
Create .net file with locations and connections between mature HVC-RA neurons in array
"""
file_RA_xy = os.path.join(dirname, "RA_xy_" + str(trial_number) + ".bin")
file_training = os.path.join(dirname, "training_neurons.bin")
file_pajek = os.path.join(dirname, "network_" + str(trial_number) + ".net")
fileMature = os.path.join(dirname, "mature_" + str(trial_number) + ".bin")
fileSuperSynapses = os.path.join(
dirname, "RA_RA_super_connections_" + str(trial_number) + ".bin")
fileWeights = os.path.join(dirname,
"weights_" + str(trial_number) + ".bin")
coord_RA = reading.read_coordinates(file_RA_xy)
training_neurons = reading.read_training_neurons(file_training)
(N_RA, _, weights) = reading.read_weights(fileWeights)
(_, _, mature_indicators) = reading.read_mature_indicators(fileMature)
(_, _, super_synapses) = reading.read_synapses(fileSuperSynapses)
mature_neurons = np.where(mature_indicators == 1)[0]
#print list(mature_neurons)
#mature_neurons = range(N_RA)
num_neurons = len(mature_neurons)
# sort array with neurons and training neurons #
training_neurons.sort()
mature_neurons.sort()
with open(file_pajek, 'w') as f:
f.write("*Vertices {0}\n".format(num_neurons))
for i, neuron_id in enumerate(mature_neurons):
if neuron_id in training_neurons:
f.write('{0} "{1}" {2} {3} {4} ic Green\n'.format(
i + 1, neuron_id, coord_RA[neuron_id][0],
coord_RA[neuron_id][1], coord_RA[neuron_id][2]))
else:
f.write('{0} "{1}" {2} {3} {4} ic Yellow\n'.format(
i + 1, neuron_id, coord_RA[neuron_id][0],
coord_RA[neuron_id][1], coord_RA[neuron_id][2]))
f.write("*Arcs\n".format(num_neurons))
# write targets of HVC(RA) neurons
for i, source_id in enumerate(mature_neurons):
for target_id in super_synapses[source_id]:
try:
ind = utils.index(mature_neurons, target_id)
f.write('{0} {1} {2} c Green\n'.format(
i + 1, ind + 1, weights[source_id][target_id]))
except ValueError:
continue
def plot_activity_of_not_connected(activity_history, training_neurons, dirname,
trial_number):
"""
Plot population firing rate of neurons that do not receive active or super synapses and
are not among training set
"""
num_neurons = activity_history.shape[0]
current_trial = trial_number
time = []
not_connected_population_firing_rate = []
timepoint = 0
while current_trial >= 0:
time.append(current_trial)
fileActiveSynapses = os.path.join(
dirname, "RA_RA_active_connections_" + str(current_trial) + ".bin")
(_, _, active_synapses) = reading.read_synapses(fileActiveSynapses)
not_connected_neurons = []
num_active_inputs = np.zeros(num_neurons, np.int32)
for i in range(num_neurons):
for target in active_synapses[i]:
num_active_inputs[target] += 1
for i in range(num_neurons):
if num_active_inputs[i] == 0 and i not in training_neurons:
not_connected_neurons.append(i)
print "trial {0}; num not connected neurons = {1}".format(
current_trial, len(not_connected_neurons))
not_connected_population_firing_rate.append(
float((activity_history[not_connected_neurons].sum(
axis=0))[timepoint * trialStep]) /
float(len(not_connected_neurons)))
#print activity_history[not_connected_neurons].sum(axis=0)[current_trial]
current_trial -= trialStep
timepoint += 1
plt.figure()
plt.plot(time, not_connected_population_firing_rate)
plt.xlabel('time (# of trials)')
plt.ylabel('< firing rate of not connected neurons > (Hz)')
burstsNeuron[0]) > 1:
first_spike_time_for_fsi.append(burstsNeuron[0][0])
fsi.append(burstsNeuron[0][1] - burstsNeuron[0][0])
else:
print "Neuron {0} produced {1} bursts\n".format(
id[0], len(burstsNeuron))
plt.figure()
plt.scatter(first_spike_time_for_fsi, fsi)
plt.xlabel('First spike time (ms)')
plt.ylabel('First spike time interval (ms)')
###########################################################
# calculate delivery time of the first spike in the burst #
###########################################################
(_, _, active_synapses) = reading.read_synapses(
os.path.join(dataDir, "RA_RA_active_connections_" + str(trial) + ".bin"))
(_, _, super_synapses) = reading.read_synapses(
os.path.join(dataDir, "RA_RA_super_connections_" + str(trial) + ".bin"))
(_, _, axonal_delays_RA2I) = reading.read_axonal_delays(
os.path.join(dataDir, "axonal_delays_RA2I_" + str(trial) + ".bin"))
(_, _, axonal_delays_RA2RA) = reading.read_axonal_delays(
os.path.join(dataDir, "axonal_delays_RA2RA_" + str(trial) + ".bin"))
(_, _, axonal_delays_I2RA) = reading.read_axonal_delays(
os.path.join(dataDir, "axonal_delays_I2RA_" + str(trial) + ".bin"))
nbins = 50
f = plt.figure()
ax = f.add_subplot(111)
trial_number = 11900
fileSpikes = os.path.join(dirname,
"test/" + sim_name + "test_spike_times_soma_5.bin")
fileConnectionsRA2RA = os.path.join(
dirname,
sim_name + "RA_RA_super_connections_" + str(trial_number) + ".bin")
#fileAxonalDelaysRA2RA = os.path.join(dirname, sim_name + "axonal_delays_RA2RA_" + str(trial_number) + ".bin")
(trial_number, simulation_time, spike_times_raw,
neuron_fired) = reading.read_time_info(fileSpikes)
(
N_RA,
_,
RA2RA_targets_ID,
) = reading.read_synapses(fileConnectionsRA2RA)
#(_, _, axonal_delays_RA2RA) = reading.read_axonal_delays(fileAxonalDelaysRA2RA)
print "Number of HVC(RA) neurons = ", N_RA
#print neuron_fired
threshold = 1.0
all_first_spike_times = np.empty(N_RA, np.float32)
all_first_spike_times.fill(-100.0)
num_bursts = 0
for n, time in zip(neuron_fired, spike_times_raw):
num_bursts += 1
fileWeights = os.path.join(dirname, "weights_" + str(trial_number) + ".bin")
fileActiveSynapses = os.path.join(
dirname, "RA_RA_active_connections_" + str(trial_number) + ".bin")
fileSuperSynapses = os.path.join(
dirname, "RA_RA_super_connections_" + str(trial_number) + ".bin")
fileTraining = os.path.join(dirname, "training_neurons.bin")
fileMature = os.path.join(dirname, "mature_" + str(trial_number) + ".bin")
fileAxonalDelaysRA2RA = os.path.join(
dirname, "axonal_delays_RA2RA_" + str(trial_number) + ".bin")
#fileActive = "/home/eugene/Output/networks/chainGrowth/testGrowthDelays3/RA_RA_active_connections_5300.bin"
#(_, _, active_synapses) = reading.read_synapses(fileActive)
(_, _, axonal_delays_RA2RA) = reading.read_axonal_delays(fileAxonalDelaysRA2RA)
(_, _, active_synapses) = reading.read_synapses(fileActiveSynapses)
(_, _, super_synapses) = reading.read_synapses(fileSuperSynapses)
(_, _, weights) = reading.read_weights(fileWeights)
(_, _, mature_indicators) = reading.read_remodeled_indicators(fileMature)
training_neurons = reading.read_training_neurons(fileTraining)
print "Mature neurons: ", [
i for i in np.where(mature_indicators == 1)[0] if i not in training_neurons
]
print "Training neurons: ", training_neurons
for i in training_neurons:
print "Training neuron {0} has {1} supersynapses : {2}".format(
i, len(super_synapses[i]), super_synapses[i])
print axonal_delays_RA2RA[228][231]
fileActive = os.path.join(
dirname, "RA_RA_active_connections_" + str(trial_number) + ".bin")
fileSuper = os.path.join(
dirname, "RA_RA_super_connections_" + str(trial_number) + ".bin")
fileMature = os.path.join(dirname, "mature_" + str(trial_number) + ".bin")
fileSpikes = "/home/eugene/results/immature/clusters/test/matTrans44/test_spike_times_soma_10.bin"
#fileSpikes = "/home/eugene/Output/networks/chainGrowth/passiveDendrite/test/maturationTransition4/test_spike_times_soma_7.bin"
(_, _, mature_indicators) = reading.read_mature_indicators(fileMature)
mature_neurons = np.where(mature_indicators == 1)[0]
(trial_number, simulation_time, spike_times_raw,
neuron_fired) = reading.read_time_info(fileSpikes)
(N_RA, _, active_synapses) = reading.read_synapses(fileActive)
(N_RA, _, super_synapses) = reading.read_synapses(fileSuper)
print "Number of HVC(RA) neurons = ", N_RA
#print neuron_fired
threshold = 1.0
all_first_spike_times = np.empty(N_RA, np.float32)
all_first_spike_times.fill(-100.0)
num_bursts = 0
for n, time in zip(neuron_fired, spike_times_raw):
num_bursts += 1
network_dir = "/home/eugene/Output/networks/chainGrowth/testGrowthDelays3/"
#network_name = "out50_active30_1ms"
#sim_name = "ee0.015_ie0.03_newModel_burstOnsets_0_"
fileMature = os.path.join(network_dir, "mature_" + str(trial_number) + ".bin")
fileActiveSynapses = os.path.join(
network_dir, "RA_RA_active_connections_" + str(trial_number) + ".bin")
fileAxonalDelaysRA2RA = os.path.join(
network_dir, "axonal_delays_RA2RA_" + str(trial_number) + ".bin")
fileSpikeTimes = "/home/eugene/Output/networks/chainGrowth/matureTest/test_spike_times_soma_2.bin"
#MARGIN_LATE = 0.5 # margin for the burst coming late
MARGIN_LATE = 0.0 # margin for the burst coming late
(N_RA, _, mature_indicators) = reading.read_mature_indicators(fileMature)
(_, _, active_synapses) = reading.read_synapses(fileActiveSynapses)
(_, _, axonal_delays_RA2RA) = reading.read_axonal_delays(fileAxonalDelaysRA2RA)
print "Number of HVC(RA) neurons = ", N_RA
(_, _, spike_times_soma,
neuron_fired_soma) = reading.read_time_info(fileSpikeTimes)
#print "Dedritic spikes: ", neuron_fired_dend
#print "Dendritic spike times: ", spike_times_dend
#print "Somatic spikes: ", neuron_fired_soma
#print "Somatic spike times: ", spike_times_soma
#print ordered_soma_spikes_raw
#print ordered_soma_raw
#trial_number = 20600
trial_number = 0
fileConnections = os.path.join(
datadir, "RA_RA_super_connections_" + str(trial_number) + ".bin")
fileDelaysRA2RA = os.path.join(
datadir, "axonal_delays_RA2RA_" + str(trial_number) + ".bin")
fileMature = os.path.join(datadir, "mature_" + str(trial_number) + ".bin")
fileTraining = os.path.join(datadir, "training_neurons.bin")
#fileSpikeTimes = "/home/eugene/results/immature/clusters/test/matTrans40/test_spike_times_soma_10.bin"
#fileJitter = "/home/eugene/results/immature/clusters/test/matTrans31/jitter.bin"
fileJitter = "/home/eugene/Output/networks/chainGrowth/passiveDendrite/chain_0.5_delay_3.0/jitter.bin"
(N_RA, _, super_synapses) = reading.read_synapses(fileConnections)
(_, _, mature_indicators) = reading.read_mature_indicators(fileMature)
training_neurons = reading.read_training_neurons(fileTraining)
# reading spike times of particular trial
#(_, _, spike_times_raw, neuron_spiked) = reading.read_time_info(fileSpikeTimes)
# reading mean spike times
_, num_test_trials, \
probability_soma_spike, average_num_soma_spikes_in_trial, first_spike_times, 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(fileJitter)
print "Number of HVC(RA) neurons = ", N_RA
#==============================================================================
# first_spike_times = np.empty(N_RA, np.float32)
def write_pajek_network_subset(dirname, trial_number, N, fileSpikes):
"""
Create .net file with locations and connections between mature HVC-RA neurons in array
first N mature neurons that spiked are plotted
"""
file_RA_xy = os.path.join(dirname, "RA_xy_" + str(trial_number) + ".bin")
file_training = os.path.join(dirname, "training_neurons.bin")
file_pajek = os.path.join(dirname,
"network_subset_" + str(trial_number) + ".net")
fileMature = os.path.join(dirname, "mature_" + str(trial_number) + ".bin")
fileSuperSynapses = os.path.join(
dirname, "RA_RA_super_connections_" + str(trial_number) + ".bin")
fileWeights = os.path.join(dirname,
"weights_" + str(trial_number) + ".bin")
coord_RA = reading.read_coordinates(file_RA_xy)
training_neurons = reading.read_training_neurons(file_training)
(N_RA, _, weights) = reading.read_weights(fileWeights)
(_, _, mature_indicators) = reading.read_mature_indicators(fileMature)
(_, _, super_synapses) = reading.read_synapses(fileSuperSynapses)
#print list(mature_neurons)
#mature_neurons = range(N_RA)
# sort array with neurons and training neurons #
training_neurons.sort()
#fileDend = "/home/eugene/Output/networks/chainGrowth/passiveDendrite/test/noImmatureOut4/test_spike_times_dend_5.bin"
#fileSoma = "/home/eugene/Output/networks/chainGrowth/passiveDendrite/test/noImmatureOut4/test_spike_times_soma_5.bin"
(_, _, spike_times_soma,
neuron_fired_soma) = reading.read_time_info(fileSpikes)
ordered_soma_spikes_raw, ordered_soma_raw = zip(
*sorted(zip(spike_times_soma, neuron_fired_soma)))
first_mature_spiked = []
for spikes, neuron_ids in zip(ordered_soma_spikes_raw, ordered_soma_raw):
if len(first_mature_spiked) >= N:
break
if mature_indicators[neuron_ids[0]] == 1:
first_mature_spiked.append(neuron_ids[0])
first_mature_spiked.sort()
num_neurons = len(first_mature_spiked)
with open(file_pajek, 'w') as f:
f.write("*Vertices {0}\n".format(num_neurons))
for i, neuron_id in enumerate(first_mature_spiked):
if neuron_id in training_neurons:
f.write('{0} "{1}" {2} {3} {4} ic Green\n'.format(
i + 1, neuron_id, coord_RA[neuron_id][0],
coord_RA[neuron_id][1], coord_RA[neuron_id][2]))
else:
f.write('{0} "{1}" {2} {3} {4} ic Yellow\n'.format(
i + 1, neuron_id, coord_RA[neuron_id][0],
coord_RA[neuron_id][1], coord_RA[neuron_id][2]))
f.write("*Arcs\n".format(num_neurons))
# write targets of HVC(RA) neurons
for i, source_id in enumerate(first_mature_spiked):
for target_id in super_synapses[source_id]:
try:
ind = utils.index(first_mature_spiked, target_id)
f.write('{0} {1} {2} c Green\n'.format(
i + 1, ind + 1, weights[source_id][target_id]))
except ValueError:
continue
dirname, "RA_RA_active_connections_" + str(trial_number) + ".bin")
#fileSpikeTimes = os.path.join(dirname, "spike_times_soma_" + str(trial_number) + ".bin")
fileDelaysRA2RA = os.path.join(
dirname, "axonal_delays_RA2RA_" + str(trial_number) + ".bin")
fileMature = os.path.join(dirname, "mature_" + str(trial_number) + ".bin")
fileWeights = os.path.join(dirname, "weights_" + str(trial_number) + ".bin")
fileTraining = os.path.join(dirname, "training_neurons.bin")
#fileSpikeTimes = "/home/eugene/Output/networks/chainGrowth/passiveDendrite/test/maturationTransition4/test_spike_times_soma_7.bin"
#fileSpikeTimes = "/home/eugene/results/immature/clusters/test/matTrans44/test_spike_times_soma_10.bin"
fileSpikeTimes = "/mnt/hodgkin/eugene/results/immature/clusters/matTrans54/spike_times_soma_13400.bin"
MARGIN_LATE = 0.0 # margin for the burst coming late
(N_RA, _, active_synapses) = reading.read_synapses(fileConnections)
(_, _, spike_times_raw, neuron_spiked) = reading.read_time_info(fileSpikeTimes)
(_, _, axonal_delays_RA2RA) = reading.read_axonal_delays(fileDelaysRA2RA)
(_, _, weights) = reading.read_weights(fileWeights)
(_, _, mature_indicators) = reading.read_mature_indicators(fileMature)
training_neurons = reading.read_training_neurons(fileTraining)
print "Number of HVC(RA) neurons = ", N_RA
first_spike_times = np.empty(N_RA, np.float32)
first_spike_times.fill(-1.0)
for n, time in zip(neuron_spiked, spike_times_raw):
first_spike_times[n[0]] = time[0]
delivered_times_and_id = []
##########################################################
N_RA = 1000
source_neuron = 195
##### find all supersynaptic targets ever existing from source neuron
all_supersynapse_targets = set()
current_trial = 0
timepoint = 0
while current_trial <= trial_number:
fileSuper = os.path.join(
dirname, "RA_RA_super_connections_" + str(current_trial) + ".bin")
(N, _, super_synapses) = reading.read_synapses(fileSuper)
for target_id in super_synapses[source_neuron]:
all_supersynapse_targets.add(target_id)
timepoint += 1
current_trial += trialStep
target_neurons = list(all_supersynapse_targets)
print "Target neurons: ", target_neurons
for target in target_neurons:
print "{0} -> {1} delay = {2}".format(
source_neuron, target, axonal_delays_RA2RA[source_neuron][target])
synaptic_weights = utils.get_weight_time_sequence_from_source(
N = 1000
N_I = 200
replaced_neurons = reading.read_replaced_neurons(fileReplaced)
replaced_neurons_trial = set(replaced_neurons[starting_trial])
not_replaced_trial = [i for i in range(N) if i not in replaced_neurons_trial]
#print replaced_neurons_trial
######### coordinates ##############
coordBefore = reading.read_coordinates(fileCoordBefore)
coordAfter = reading.read_coordinates(fileCoordAfter)
######### active synapses ##############
(_, _, active_synapses_before) = reading.read_synapses(fileActiveBefore)
(_, _, active_synapses_after) = reading.read_synapses(fileActiveAfter)
######### super synapses ###############
(_, _, super_synapses_before) = reading.read_synapses(fileSuperBefore)
(_, _, super_synapses_after) = reading.read_synapses(fileSuperAfter)
############# HVC-RA -> HVC-I connections #############
(_, targets_id_RA2I_before, weights_RA2I_before, \
syn_lengths_RA2I_before, axonal_delays_RA2I_before) = reading.read_connections(fileRA2IBefore)
(_, targets_id_RA2I_after, weights_RA2I_after, \
syn_lengths_RA2I_after, axonal_delays_RA2I_after) = reading.read_connections(fileRA2IAfter)
############# HVC-I -> HVC-RA connections #############
(_, targets_id_I2RA_before, weights_I2RA_before, \
neuron_fired_soma_noDelays) = reading.read_time_info(fileSoma_noDelays)
(_, _, spike_times_interneuron_noDelays,
interneuron_fired_noDelays) = reading.read_time_info(fileInterneuron_noDelays)
(_, _, spike_times_dend_zeroDelays,
neuron_fired_dend_zeroDelays) = reading.read_time_info(fileDend_zeroDelays)
(_, _, spike_times_soma_zeroDelays,
neuron_fired_soma_zeroDelays) = reading.read_time_info(fileSoma_zeroDelays)
(_, _, spike_times_interneuron_zeroDelays, interneuron_fired_zeroDelays
) = reading.read_time_info(fileInterneuron_zeroDelays)
(_, _, weights_noDelays) = reading.read_weights(fileWeights_noDelays)
(_, _, weights_zeroDelays) = reading.read_weights(fileWeights_zeroDelays)
(_, _,
active_synapses_noDelays) = reading.read_synapses(fileActiveSynapses_noDelays)
(_, _, active_synapses_zeroDelays
) = reading.read_synapses(fileActiveSynapses_zeroDelays)
print np.array_equal(np.array(spike_times_dend_noDelays),
np.array(spike_times_dend_zeroDelays))
print np.array_equal(np.array(neuron_fired_dend_noDelays),
np.array(neuron_fired_dend_zeroDelays))
print np.array_equal(np.array(spike_times_soma_noDelays),
np.array(spike_times_soma_zeroDelays))
print np.array_equal(np.array(neuron_fired_soma_noDelays),
np.array(neuron_fired_soma_zeroDelays))
print np.array_equal(np.array(spike_times_interneuron_noDelays),
np.array(spike_times_interneuron_zeroDelays))