<jupyter_start><jupyter_text>6<jupyter_code>N_in, N_out = 400, 100
weights = np.random.uniform(size=(N_out,N_in))
normalization = np.sqrt(np.sum(weights**2,axis=1)) 
weights /= normalization[:, np.newaxis]
def forward_input(r_in,weights):
    return np.dot(weights,r_in)
r_in = activation(pos,firing_loc,sigma)
for t in np.arange(len(r_in)):
    h[t] = forward_input(r_in[t],weights)
    rp[t+1] = rp[t]+1/tau_plus*(h[t]-rp[t]-rm[t])
    rm[t+1] = rm[t]+1/tau_min*(h[t]-rm[t])
    r_out[i,t] = 2/np.pi*np.arctan(g*(rp[i,t]-mu))<jupyter_output><empty_output>
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