# Set the random set for reproducibility
seed = 31255433
np.random.seed(seed)
# Create indexes
signal_indexes, delay_indexes, stimuli_indexes = create_standar_indexes(
dt, dt_kernel, dt_stimuli, kernel_size, Nt_simulation)
working_indexes, kernel_times = create_extra_indexes(kernel_size,
Nt_simulation)
# Initialize the signal array to be filled
firing_rate = np.zeros(Nt_simulation)
# Create the stimuli
stimuli = sine_grating(dx, lx, dy, ly, A, K, Phi, Theta, dt_stimuli, N_stimuli,
w)
# Chose the particular cell
xc = 0
yc = 0
# Create the kernel
kernel = create_kernel(dx,
lx,
dy,
ly,
sigma_surround,
sigma_center,
dt_kernel,
kernel_size,
inverse=1,
seed = 1053
np.random.seed(seed)
# Create indexes
signal_indexes, delay_indexes, stimuli_indexes = create_standar_indexes(dt, dt_kernel, dt_stimuli, kernel_size, Nt_simulation)
working_indexes, kernel_times = create_extra_indexes(kernel_size, Nt_simulation)
# Initialize the signal array to be filled
firing_rate_on1 = np.zeros(Nt_simulation)
firing_rate_off1 = np.zeros(Nt_simulation)
firing_rate_on2 = np.zeros(Nt_simulation)
firing_rate_off2 = np.zeros(Nt_simulation)
# Create the stimuli
stimuli1 = sine_grating(dx, lx, dy, ly, A1, K, Phi, Theta, dt_stimuli, N_stimuli, w)
stimuli2 = sine_grating(dx, lx, dy, ly, A2, K, Phi, Theta, dt_stimuli, N_stimuli, w)
# Chose the particular cell
xc = 0
yc = 0
# Create the kernels
kernel_on = create_kernel(dx, lx, dy, ly, sigma_surround, sigma_center,
dt_kernel, kernel_size, inverse=1, x_tra=0, y_tra=0)
kernel_off = create_kernel(dx, lx, dy, ly, sigma_surround, sigma_center,
dt_kernel, kernel_size, inverse=-1, x_tra=0, y_tra=0)
# Calculate the firing rate through convolution
for index in signal_indexes:
# Space parameters dx = 0.05 dy = 0.05 lx = 5.0 # In degrees ly = 5.0 # In degrees # sine grating spatial parameters K = 1.0 # cycles per degree Phi = 0 * np.pi Theta = 0 * np.pi A = 1.0 # Temporal frequency of sine grating w = 3 # Hz stimuli = sine_grating(dx, lx, dy, ly, A, K, Phi, Theta, dt_stimuli, N_stimuli, w) Z = stimuli[0,...] plt.imshow(Z, extent=[-lx/2,lx/2,ly/2,-ly/2]) plt.colorbar() plt.show() # x = np.arange(-lx/2,lx/2,dx) # y = Z[0,:] # plt.plot(x,y) # plt.show() # # t = np.arange(0, N_stimuli * dt_stimuli, dt_stimuli) # y = stimuli[:,0,0] # plt.plot(t,y)
# Create indexes
signal_indexes, delay_indexes, stimuli_indexes = create_standar_indexes(
dt, dt_kernel, dt_stimuli, kernel_size, Nt_simulation)
working_indexes, kernel_times = create_extra_indexes(kernel_size,
Nt_simulation)
# Initialize the signal array to be filled
firing_rate_on1 = np.zeros(Nt_simulation)
firing_rate_off1 = np.zeros(Nt_simulation)
firing_rate_on2 = np.zeros(Nt_simulation)
firing_rate_off2 = np.zeros(Nt_simulation)
# Create the stimuli
stimuli1 = sine_grating(dx, lx, dy, ly, A1, K, Phi, Theta, dt_stimuli,
N_stimuli, w)
stimuli2 = sine_grating(dx, lx, dy, ly, A2, K, Phi, Theta, dt_stimuli,
N_stimuli, w)
# Chose the particular cell
xc = 0
yc = 0
# Create the kernels
kernel_on = create_kernel(dx,
lx,
dy,
ly,
sigma_surround,
sigma_center,
dt_kernel,