def performance(spikes, pars, stimulus):
"""
Calculate the response of a model neuron, given a set of filters and
a stimulus. Also calculate the cross-correlation with the real spikes
as a measure of performance.
"""
k, Q, mu = gqm.splitpars(pars)
firing_rate = gqm.gqm_neuron(k, Q, mu, st.frame_duration)(stimulus)
cross_corr = np.corrcoef(spikes, firing_rate)[0, 1]
return cross_corr, firing_rate
sp_tr, sp_te, stim_tr, stim_te = train_test_split(spikes, stimulus,
test_size=val_split_size,
split_pos=val_split_pos)
res = gqm.minimize_loglikelihood(np.zeros((stimdim, fl)),
np.zeros((stimdim, fl, fl)), 0,
stim_tr,
st.frame_duration,
sp_tr,
minimize_disp=True,
method='BFGS')
elapsed = time.time()-start
print(f'Time elapsed: {elapsed/60:6.1f} mins for cell {i}')
k_out, Q_out, mu_out = gqm.splitpars(res.x)
kall[i, ...] = k_out
Qall[i, ...] = Q_out
muall[i] = mu_out
firing_rate = gqm.gqm_neuron(k_out, Q_out, mu_out, st.frame_duration)(stim_te)
cross_corr = np.corrcoef(sp_te, firing_rate)[0, 1]
cross_corrs[i] = cross_corr
#%%
fig, axes = plt.subplots(stimdim, 5, figsize=(15, 5))
plt.rc('font', size=8)
for j in range(stimdim):
axk = axes[j, 0]
if j <= 1:
axk.plot(t, sta[j], color='grey', label='STA')
axk.plot(t, k_out[j, ...], label='k (GQM)')
np.zeros((stimdim, st.filter_length,
st.filter_length)),
0,
stimulus,
st.frame_duration,
spikes,
method='BFGS',
callback=optim_tracker)
elapsed = time.time() - start
print(f'Time elapsed: {elapsed/60:6.1f} mins for cell {i}')
all_pars_progress.append(pars_progress)
all_res.append(res)
plt.plot(gqm.splitpars(res.x)[0].T)
plt.title('Final linear filters')
plt.show()
spikes = st.binnedspiketimes(i)
if res.nit > 1000:
break
cc_progress = np.zeros(res.nit)
for j, pars in enumerate(pars_progress):
cc_progress[j], fr = performance(spikes, pars, stimulus)
# plt.plot(ki.T)
# plt.title(j)
# plt.show()