def plot_2d_input_weights():
name = 'XeAe'
weights = get_2d_input_weights()
fig = b2.figure(fig_num, figsize = (18, 18))
im2 = b2.imshow(weights, interpolation = "nearest", vmin = 0, vmax = wmax_ee, cmap = cmap.get_cmap('hot_r'))
b2.colorbar(im2)
b2.title('weights of connection' + name)
fig.canvas.draw()
return im2, fig
def plot_2d_input_weights():
name = 'XeAe'
weights = get_2d_input_weights()
fig = b2.figure(fig_num, figsize = (18, 18))
im2 = b2.imshow(weights, interpolation = "nearest", vmin = 0, vmax = wmax_ee, cmap = cmap.get_cmap('hot_r'))
b2.colorbar(im2)
b2.title('weights of connection' + name)
fig.canvas.draw()
return im2, fig
range=(0., 1.))[0]
weights_mu = b2.array(synapse_mu.w)
mu_weight_histograms = b2.empty((N_bins, N_out))
for i in range(N_out):
# brian2 uses i->j synapse syntax, while I use j->i
mask = synapse_mu.j == i
mu_weight_histograms[:, i] = np.histogram(weights_mu[mask] / w_max,
bins=N_bins,
density=True,
range=(0., 1.))[0]
b2.figure(figsize=(4, 4))
im = b2.imshow(
rates_weight_histograms,
origin='lower',
extent=[postsyn_rates[0] / b2.Hz, postsyn_rates[-1] / b2.Hz, 0, 1],
aspect='auto',
cmap=b2.plt.cm.gray_r)
# b2.colorbar(label='probability density')
b2.xlabel("Postsynaptic Firing Rate (Hz)")
# b2.ylabel(r"$\frac{w}{w_{max}}$", rotation=0)
b2.ylabel("$w/w_{max}$")
b2.title(
'Synaptic Strength Distributions\nas function of postsynaptic firing rate'
)
b2.savefig('images_and_animations/synaptic_strengths_vs_firing_rates.png')
# b2.show()
b2.figure(figsize=(4, 4))
im = b2.imshow(mu_weight_histograms,
origin='lower',