def write_pajek_hvcRA_coord(dirname, trial_number):
"""
Create .net file with locations of HVC-RA neurons in array.
Mature neurons are highlighted
"""
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")
coord_RA = reading.read_coordinates(file_RA_xy)
training_neurons = reading.read_training_neurons(file_training)
(_, _, mature_indicators) = reading.read_mature_indicators(fileMature)
mature_neurons = np.where(mature_indicators == 1)[0]
num_neurons = coord_RA.shape[0]
# 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 in range(num_neurons):
if i in training_neurons:
f.write('{0} "{1}" {2} {3} {4} ic Green\n'.format(
i + 1, i, coord_RA[i][0], coord_RA[i][1], coord_RA[i][2]))
elif i in mature_neurons:
f.write('{0} "{1}" {2} {3} {4} ic Black\n'.format(
i + 1, i, coord_RA[i][0], coord_RA[i][1], coord_RA[i][2]))
else:
f.write('{0} "{1}" {2} {3} {4} ic Yellow\n'.format(
i + 1, i, coord_RA[i][0], coord_RA[i][1], coord_RA[i][2]))
def estimate_oscillations(filenameTrial, filenameMature):
"""
Estimate oscillation prominence for mature neurons in trial
"""
(_, _, mature_indicators) = reading.read_mature_indicators(filenameMature)
mature_neurons = np.where(mature_indicators == 1)[0]
# plot somatic spikes and burst density for a single trial
mature_neurons_set = set(mature_neurons)
spike_times_s, neuron_id_s, ordered_spikes_s, neuron_ordered_id_s = utils.getSpikes(
os.path.join(testDir, filenameTrial))
ordered_spikes_s_mature = []
neuron_ordered_id_s_mature = []
for spikes, neuron_id in zip(spike_times_s, neuron_id_s):
if neuron_id[0] in mature_neurons_set:
ordered_spikes_s_mature.append(spikes)
neuron_ordered_id_s_mature.append(neuron_id)
ordered_spikes_s_mature = sorted(ordered_spikes_s_mature)
smallest_spike_time = ordered_spikes_s_mature[0][0]
for spikes in ordered_spikes_s_mature:
for i in range(len(spikes)):
spikes[i] -= smallest_spike_time
first_spikes_s = [s[0] for s in ordered_spikes_s_mature]
time, burst_density = utils.calculate_burst_density_hist(
first_spikes_s, BIN_WIDTH, -10.0, 600.0)
f = plt.figure()
plt.suptitle('Single trial')
ax = f.add_subplot(111)
ax.plot(time, burst_density)
ax.set_ylabel('Burst density (1/ms)')
ax.set_xlabel('Time (ms)')
ax.set_xlim([0, 250])
ax.set_ylim([0, 11])
first_nonzero = np.where(burst_density > 0)[0][0]
last_nonzero = np.where(burst_density > 0)[0][-1]
mean_bd = np.mean(burst_density[first_nonzero:last_nonzero])
std_bd = np.std(burst_density[first_nonzero:last_nonzero])
print "Interval for non-zero burst density: ", time[first_nonzero], time[
last_nonzero]
print "Mean burst density: ", mean_bd
print "Std burst density: ", std_bd
print "Std / mean: ", std_bd / mean_bd
return std_bd / mean_bd
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 get_maturation_time_sequence(dirname, end_trial, trialStep, neurons):
"""
Outputs excitatory conductance input from source_neurons to target_neuron at different
points during simulation
"""
num_timepoints = end_trial / trialStep + 1
maturation_indicators = np.zeros((len(neurons), num_timepoints), np.float32)
current_trial = 0
timepoint = 0
while current_trial <= end_trial:
fileMature = os.path.join(dirname, "mature_" + str(current_trial) + ".bin")
(_, _, mat_indicators) = reading.read_mature_indicators(fileMature)
for i, neuron_id in enumerate(neurons):
maturation_indicators[i][timepoint] = mat_indicators[neuron_id]
timepoint += 1
current_trial += trialStep
return maturation_indicators
all_first_spikes_in_burst = [
s - min_first_spike for s in all_first_spikes_in_burst
]
#ordered_dend_spikes = [d - min_dend_spike_time for d in ordered_dend_spikes]
time, burst_density = utils.calculate_burst_density(
sorted(all_first_spikes_in_burst), BIN_WIDTH)
#start_time = 0.0
#end_time = 120.0
#########################################################################
# calculate continuity for the burst pattern produced by mature neurons #
#########################################################################
(_, _, mature_indicators) = reading.read_mature_indicators(
os.path.join(dataDir, "mature_" + str(trial) + ".bin"))
print "Number of mature HVC-RA: ", len(np.where(mature_indicators == 1)[0])
min_first_spike_mature = 1e6
max_first_spike_mature = -1e6
for id in np.where(mature_indicators == 1)[0]:
for first_spike in first_spikes_in_bursts[id]:
if first_spike < min_first_spike_mature:
min_first_spike_mature = first_spike
if first_spike > max_first_spike_mature:
max_first_spike_mature = first_spike
min_first_spike_mature -= min_first_spike
max_first_spike_mature -= min_first_spike
import matplotlib.pyplot as plt
import numpy as np
TRAINING_KICK_TIME = 100.0
#filename = "/home/eugene/Output/networks/chainGrowth/passiveDendrite/test/maturationTransition4/jitter.bin"
#fileMature = "/home/eugene/Output/networks/chainGrowth/passiveDendrite/maturationTransition4/mature_16500.bin"
filename = "/mnt/hodgkin/eugene/results/immature/clusters/test/matTrans62/trial4200/jitter.bin"
fileMature = "/mnt/hodgkin/eugene/results/immature/clusters/matTrans62/mature_4200.bin"
N, num_test_trials, \
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(filename)
(_, _, mature_indicators) = reading.read_mature_indicators(fileMature)
mature_neurons = np.where(mature_indicators == 1)[0]
print mature_neurons
print "Number of mature: ", len(mature_neurons)
#mature_neurons = range(N)
mean_first_soma_spike_time_mature = mean_first_soma_spike_time[mature_neurons]
mean_first_dend_spike_time_mature = mean_first_dend_spike_time[mature_neurons]
std_first_soma_spike_time_mature = std_first_soma_spike_time[mature_neurons]
std_first_dend_spike_time_mature = std_first_dend_spike_time[mature_neurons]
probability_soma_spike_mature = probability_soma_spike[mature_neurons]
probability_dend_spike_mature = probability_dend_spike[mature_neurons]
average_num_soma_spikes_in_trial_mature = average_num_soma_spikes_in_trial[
mature_neurons]
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
num_cols = 3
#dirname = "/home/eugene/Output/networks/chainGrowth/passiveDendrite/maturationTransition4/"
dirname = "/mnt/hodgkin/eugene/results/immature/clusters/matTrans62/"
trial_number = 32400
fileCoordRA = os.path.join(dirname, "RA_xy_" + str(trial_number) + ".bin")
fileTraining = os.path.join(dirname, "training_neurons.bin")
#fileSoma = os.path.join(dirname, "spike_times_soma_" + str(trial_number) + ".bin")
#fileSoma = "/home/eugene/Output/networks/chainGrowth/passiveDendrite/test/noImmatureOut8/test_spike_times_soma_5.bin"
fileSoma = "/mnt/hodgkin/eugene/results/immature/clusters/test/matTrans62/trial32400/test_spike_times_soma_5.bin"
coord = reading.read_coordinates(fileCoordRA)
training_neurons = reading.read_training_neurons(fileTraining)
(_, _, mature_indicators) = reading.read_mature_indicators(os.path.join(dirname, "mature_"+str(trial_number)+".bin"))
(_, _, spike_times_soma, neuron_fired_soma) = reading.read_time_info(fileSoma)
print "Number of spiked neurons: ",len(spike_times_soma)
for spikes, neuron_id in zip(spike_times_soma, neuron_fired_soma):
if len(spikes) > 6:
print "Neuron {0} produced {1} spikes".format(neuron_id[0], len(spikes))
#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
