# Defines the function to create a time-filtered Gaussian noise.
#
# FiltGNoise(Mean_FRate,tauFilt,N,numSteps,dt):
# 1. [Hz] Mean firing rate
# 2. [ms] Filtering time constant
# 3. Number of neurons
# 4. Number of steps
# 5. [ms] time step
import numpy as np
def FiltGNoise(Mean_FRate,tauFilt,N,numSteps,dt):
GaussianNoise = np.random.normal(0,1,(numSteps,N)) # Gaussian noise
FilteredGaussianNoise = GaussianNoise*0. # Initialization of the filtered Gaussian noise
# Make the time-filtered Gaussian noise signal
step_tauFilt = dt/tauFilt # just to make the simulation faster
for t in range(numSteps)[1:]:
FilteredGaussianNoise[t] = FilteredGaussianNoise[t-1] - (FilteredGaussianNoise[t-1]-GaussianNoise[t-1])*step_tauFilt
# Normalize it to a maximum value of 1:
for i in range(N):
FilteredGaussianNoise[:,i] = FilteredGaussianNoise[:,i]/np.max(FilteredGaussianNoise[:,i])
# Apply the rate threshold:
rth = 0.2
FilteredGaussianNoise = FilteredGaussianNoise - rth
# Rectify it:
FilteredGaussianNoise[FilteredGaussianNoise<0.] = 0.
# Make the mean firing rate equal to Mean_FRate:
for i in range(N):
FilteredGaussianNoise[:,i] = FilteredGaussianNoise[:,i]/np.mean(FilteredGaussianNoise[:,i])*(Mean_FRate/1000.)
return FilteredGaussianNoise |