def find_silent_neurons(neurons, num_trials, dataDir):
"""
Finds neurons that did not fire in any of the training trials
"""
num_successful_trials = [
] # number of trials in which dendritic bursts were produced
for neuron_id in neurons:
counter = 0 # counter to count successful trials
for n in range(num_trials):
filename = dataDir + "RA" + str(neuron_id) + "_trial" + str(
n + 1) + ".bin"
(t, Vs, _, _, _, Vd, _, _, _, _, _, _, _, _, _, _, _,
_) = reading.read_hh2(filename)
burst_times, burst_indices = get_burst_times(
t, Vd) # obtain dendritic burst times
if len(burst_times) > 0:
counter += 1
num_successful_trials.append(counter)
not_fired = [
n for i, n in enumerate(neurons) if num_successful_trials[i] == 0
]
return not_fired, num_successful_trials
def get_burst_times(filename):
"""
Extracts dendritic burst indices from file with HVC(RA) data
"""
(t, _, _, _, _, Vd, _, _, _, _, _, _, _, _, _, _, _, _) = reading.read_hh2(filename)
burst_indices = []
burst_times = []
flag = False
for i, v in enumerate(Vd):
if flag == False and v >= DENDRITIC_THRESHOLD:
flag = True
elif flag == True and v < DENDRITIC_THRESHOLD:
burst_indices.append(i)
burst_times.append(t[i])
flag = False
return burst_times, burst_indices
def average_inh_input_burst_difference_one_neuron(neuron_id, num_trials):
"""
Computes average time difference between inhibitory inputs and dendritic bursts
for the same neuron over num_trials trials
"""
dt_all_trials = []
num_successful_trials = 0 # number of trials when neuron fired
# store time differences for all trials in one array
for i in range(1, num_trials + 1):
filename = dataDir + "RA" + str(neuron_id) + "_trial" + str(i) + ".bin"
(t, Vs, _, _, _, Vd, _, _, _, _, Gexc_d, Ginh_d, _, _, _, _, _,
_) = reading.read_hh2(filename)
dt = inh_input_burst_difference(t, Vd, Ginh_d)
if len(dt) > 0:
dt_all_trials.extend(dt)
num_successful_trials += 1
dt_all_trials = sorted(dt_all_trials)
#print dt_all_trials
if len(dt_all_trials) == 0:
print("dt_all_trials is empty for neuron {0}".format(neuron_id))
else:
min_dt = np.floor(min(dt_all_trials))
if min_dt % 2 == 0:
min_dt -= 1
bins = np.arange(
min_dt,
np.ceil(max(dt_all_trials)) + INHIBITORY_SPIKE_RESOLUTION,
INHIBITORY_SPIKE_RESOLUTION)
hist, bins = np.histogram(dt_all_trials, bins=bins)
hist = hist / float(num_successful_trials)
return hist, bins
"""
Created on Wed Feb 8 17:43:01 2017
@author: jingroup
Script plots membrane traces for same HVC(RA) neuron
"""
import reading
import matplotlib.pyplot as plt
file1 = "/home/eugene/Output/networks/RandomChainTest240217/RA/RA256_trial1.bin"
file2 = "/home/eugene/Output/networks/RandomChainTest240217/RA/RA256_trial2.bin"
file3 = "/home/eugene/Output/networks/RandomChainTest240217/RA/RA256_trial3.bin"
(t_1, Vs_1, _, _, _, Vd_1, _, _, _, _, Gexc_d_1, Ginh_d_1, _, _, _, _, _,
_) = reading.read_hh2(file1)
(t_2, Vs_2, _, _, _, Vd_2, _, _, _, _, Gexc_d_2, Ginh_d_2, _, _, _, _, _,
_) = reading.read_hh2(file2)
(t_3, Vs_3, _, _, _, Vd_3, _, _, _, _, Gexc_d_3, Ginh_d_3, _, _, _, _, _,
_) = reading.read_hh2(file3)
f = plt.figure()
ax1 = f.add_subplot(311)
ax1.plot(t_1, Vs_1, 'r')
ax1.plot(t_2, Vs_2, 'b')
ax1.plot(t_3, Vs_3, 'm')
ax1.set_ylabel("Vs (mV)")
ax2 = f.add_subplot(312)
#simFileAbs = os.path.join(script_dir, simFileRel) #testFileAbs = os.path.join(script_dir, testFileRel) #print repr(fileContent) #print dataPointsNumber[0] #print len(fileContent[8:]) #print voltage #print time #print len(voltage) #print len(time) (t, Vs, Is, n, h, Vd, Id, r, c, Ca, Gexc_d, Ginh_d, Gexc_s, Ginh_s, Ei, flag, Nsoma, Ndend) = reading.read_hh2(simFileAbs) print Nsoma #print "Vs", Vs #print "s_soma", s_s #print "s_dend", s_d print "Ei = ", Ei[100] print_time_state(0) print Vs print t #print(len(Vd)) #print(n[0]) #print(n[11999])
def calculate_average_input_conductance_for_neuron(neuron_id, num_trials,
dataDir):
"""
Calculates average input conductance for neuron neuron_id. Average is taken
for num_trials and is aligned to neuron's dendritic burst
"""
num_points_in_window = int(WINDOW_SIZE /
TIMESTEP) # number of datapoints inside window
# if even, add one more datapoint
if num_points_in_window % 2 == 0:
num_points_in_window += 1
half_window = num_points_in_window / 2
#print half_window
#print num_points_in_window
exc_input = np.zeros((num_trials, num_points_in_window), np.float32)
inh_input = np.zeros((num_trials, num_points_in_window), np.float32)
successful_trials = np.zeros(
num_trials,
np.bool) # array with bools indicating trials with dendritic bursts
for n in range(num_trials):
filename = dataDir + "RA" + str(neuron_id) + "_trial" + str(n +
1) + ".bin"
(t, Vs, _, _, _, Vd, _, _, _, _, Gexc_d, Ginh_d, _, _, _, _, _,
_) = reading.read_hh2(filename)
burst_times, burst_indices = get_burst_times(
t, Vd) # obtain dendritic burst times
if len(burst_times) > 0:
successful_trials[n] = True
# loop through all dendritic burst times
for burst_index in burst_indices:
#for i in range(num_points_in_window):
#exc_input[n][i] += Gexc_d[burst_index - half_window + i]
#inh_input[n][i] += Ginh_d[burst_index - half_window + i]
exc_input[n] += Gexc_d[(burst_index -
half_window):(burst_index +
half_window + 1)]
inh_input[n] += Ginh_d[(burst_index -
half_window):(burst_index +
half_window + 1)]
# average over number of dendritic bursts:
exc_input[n] = exc_input[n] / float(len(burst_indices))
inh_input[n] = inh_input[n] / float(len(burst_indices))
#print exc_input.shape
t_input = np.linspace(-half_window * TIMESTEP, half_window * TIMESTEP,
num_points_in_window)
# if no dendritic bursts was produced
if np.sum(successful_trials) == 0:
average_exc_input = np.zeros(num_points_in_window, np.float32)
average_inh_input = np.zeros(num_points_in_window, np.float32)
return t_input, average_exc_input, average_inh_input
else:
average_exc_input = np.sum(exc_input[successful_trials],
axis=0) / np.sum(successful_trials)
average_inh_input = np.sum(inh_input[successful_trials],
axis=0) / np.sum(successful_trials)
return t_input, average_exc_input, average_inh_input