from network import run_simulation
import numpy as np
from brian.stdunits import ms
ree_steps = np.linspace(1, 4, 13)
ff = np.zeros_like(ree_steps)
nnans = np.zeros_like(ree_steps)
nffs = np.zeros_like(ree_steps)
for i, ree in enumerate(ree_steps):
# run simulation with one network, nine trials
data = run_simulation(trials=10, ree=ree, verbose=False, winlen=100 * ms, t_stim=1000)
# take only excitatory neurons
data = data[0,:,:4000,:]
# compute fano factor
fano_factor = (np.var(data, axis=0)/np.mean(data, axis=0)).flatten()
nnans[i] = np.sum(np.isnan(fano_factor))
nffs[i] = np.sum(np.isfinite(fano_factor))
# save mean over neurons and time windows without nans
ff[i] = np.mean(fano_factor[np.isfinite(fano_factor)])
print "ree = {} done".format(ree)
print "ff = {}".format(ff[i])
np.save('Data/ff_vs_ree_working', [ree_steps, ff, nnans, nffs])
from network import run_simulation
import numpy as np
from brian.stdunits import ms
ree_steps = np.linspace(1, 4, 13)
ff = np.zeros_like(ree_steps)
nnans = np.zeros_like(ree_steps)
nffs = np.zeros_like(ree_steps)
for i, ree in enumerate(ree_steps):
# run simulation with one network, nine trials
data = run_simulation(trials=10,
ree=ree,
verbose=False,
winlen=100 * ms,
t_stim=1000)
# take only excitatory neurons
data = data[0, :, :4000, :]
# compute fano factor
fano_factor = (np.var(data, axis=0) / np.mean(data, axis=0)).flatten()
nnans[i] = np.sum(np.isnan(fano_factor))
nffs[i] = np.sum(np.isfinite(fano_factor))
# save mean over neurons and time windows without nans
ff[i] = np.mean(fano_factor[np.isfinite(fano_factor)])
print "ree = {} done".format(ree)
print "ff = {}".format(ff[i])
np.save('Data/ff_vs_ree_working', [ree_steps, ff, nnans, nffs])
from network import run_simulation
import numpy as np
import matplotlib.pyplot as plt
from brian.stdunits import ms, mV
ree_steps = np.linspace(1, 4, 7)
ff = np.zeros_like(ree_steps)
nnans = np.zeros_like(ree_steps)
for i, ree in enumerate(ree_steps):
# run simulation with one network, nine trials
data = run_simulation(trials=4, ree=ree, verbose=False, winlen=100 * ms)
# take only excitatory neurons
data = data[0, :, :4000, :]
# compute fano factor
fano_factor = (np.var(data, axis=0) / np.mean(data, axis=0)).flatten()
nnans[i] = np.sum(np.isnan(fano_factor))
# save mean over neurons and time windows without nans
ff[i] = np.mean(fano_factor[np.isfinite(fano_factor)])
print "ree = {} done".format(ree)
print "ff = {}".format(ff[i])
np.save("ff_vs_ree", [ree_steps, ff, nnans])
plt.plot(ree_steps, ff)
plt.show()