def record_trial(self, trial_idx, inputs, correct_input, wta_net, wta_monitor):
self.trial_inputs[:,trial_idx]=inputs
e_rate_0 = wta_monitor.monitors['excitatory_rate_0'].smooth_rate(width= 5 * ms, filter = 'gaussian')
e_rate_1 = wta_monitor.monitors['excitatory_rate_1'].smooth_rate(width= 5 * ms, filter = 'gaussian')
i_rate = wta_monitor.monitors['inhibitory_rate'].smooth_rate(width= 5 * ms, filter = 'gaussian')
if self.record_firing_rates:
self.pop_rates['excitatory_rate_0'].append(e_rate_0)
self.pop_rates['excitatory_rate_1'].append(e_rate_1)
self.pop_rates['inhibitory_rate'].append(i_rate)
rt, choice = get_response_time(np.array([e_rate_0, e_rate_1]), self.sim_params.stim_start_time,
self.sim_params.stim_end_time, upper_threshold = self.network_params.resp_threshold,
dt = self.sim_params.dt)
correct = choice == correct_input
if choice>-1:
print 'response time = %.3f correct = %d' % (rt, int(correct))
else:
print 'no response!'
self.num_no_response+=1
self.trial_rt[0,trial_idx]=rt
self.trial_resp[0,trial_idx]=choice
self.trial_correct[0,trial_idx]=correct
self.correct_avg[0,trial_idx] = (np.sum(self.trial_correct))/(trial_idx+1)
for conn in self.record_connections:
self.trial_weights[conn].append(wta_net.connections[conn].W.todense())
def plot_network_firing_rates(e_rates, sim_params, network_params, std_e_rates=None, i_rate=None, std_i_rate=None,
plt_title=None, labels=None, ax=None):
rt, choice = get_response_time(e_rates, sim_params.stim_start_time, sim_params.stim_end_time,
upper_threshold = network_params.resp_threshold, dt = sim_params.dt)
if ax is None:
figure()
max_rates=[network_params.resp_threshold]
if i_rate is not None:
max_rates.append(np.max(i_rate[500:]))
for i in range(network_params.num_groups):
max_rates.append(np.max(e_rates[i,500:]))
max_rate=np.max(max_rates)
if i_rate is not None:
ax=subplot(211)
elif ax is None:
ax=subplot(111)
rect=Rectangle((0,0),(sim_params.stim_end_time-sim_params.stim_start_time)/ms, max_rate+5,
alpha=0.25, facecolor='yellow', edgecolor='none')
ax.add_patch(rect)
for idx in range(network_params.num_groups):
label='e %d' % idx
if labels is not None:
label=labels[idx]
time_ticks=(np.array(range(e_rates.shape[1]))*sim_params.dt)/ms-sim_params.stim_start_time/ms
baseline,=ax.plot(time_ticks, e_rates[idx,:], label=label)
if std_e_rates is not None:
ax.fill_between(time_ticks, e_rates[idx,:]-std_e_rates[idx,:], e_rates[idx,:]+std_e_rates[idx,:], alpha=0.5,
facecolor=baseline.get_color())
ylim(0,max_rate+5)
ax.plot([0-sim_params.stim_start_time/ms, (sim_params.trial_duration-sim_params.stim_start_time)/ms],
[network_params.resp_threshold/hertz, network_params.resp_threshold/hertz], 'k--')
ax.plot([rt,rt],[0, max_rate+5],'k--')
legend(loc='best')
ylabel('Firing rate (Hz)')
if plt_title is not None:
title(plt_title)
if i_rate is not None:
ax=subplot(212)
rect=Rectangle((0,0),(sim_params.stim_end_time-sim_params.stim_start_time)/ms, max_rate+5,
alpha=0.25, facecolor='yellow', edgecolor='none')
ax.add_patch(rect)
label='i'
if labels is not None:
label=labels[network_params.num_groups]
time_ticks=(np.array(range(len(i_rate)))*sim_params.dt)/ms-sim_params.stim_start_time/ms
baseline,=ax.plot(time_ticks, i_rate, label=label)
if std_i_rate is not None:
ax.fill_between(time_ticks, i_rate-std_i_rate, i_rate+std_i_rate, alpha=0.5, facecolor=baseline.get_color())
ylim(0,max_rate+5)
ax.plot([rt,rt],[0, max_rate],'k--')
ylabel('Firing rate (Hz)')
xlabel('Time (ms)')
def run_rl_simulation(mat_file, alpha=0.4, beta=5.0, background_freq=None, p_dcs=0*pA, i_dcs=0*pA, dcs_start_time=0*ms,
output_file=None):
mat = scipy.io.loadmat(mat_file)
prob_idx=-1
mags_idx=-1
for idx,(dtype,o) in enumerate(mat['store']['dat'][0][0].dtype.descr):
if dtype=='probswalk':
prob_idx=idx
elif dtype=='mags':
mags_idx=idx
prob_walk=mat['store']['dat'][0][0][0][0][prob_idx]
mags=mat['store']['dat'][0][0][0][0][mags_idx]
prob_walk=prob_walk.astype(np.float32, copy=False)
mags=mags.astype(np.float32, copy=False)
mags /= 100.0
wta_params=default_params()
wta_params.input_var=0*Hz
sim_params=simulation_params()
sim_params.p_dcs=p_dcs
sim_params.i_dcs=i_dcs
sim_params.dcs_start_time=dcs_start_time
exp_rew=np.array([0.5, 0.5])
if background_freq is None:
background_freq=(beta-161.08)/-.17
wta_params.background_freq=background_freq
trials=prob_walk.shape[1]
sim_params.ntrials=trials
vals=np.zeros(prob_walk.shape)
choice=np.zeros(trials)
rew=np.zeros(trials)
rts=np.zeros(trials)
inputs=np.zeros(prob_walk.shape)
if output_file is not None:
f = h5py.File(output_file, 'w')
f.attrs['alpha']=alpha
f.attrs['beta']=beta
f.attrs['mat_file']=mat_file
f_sim_params=f.create_group('sim_params')
for attr, value in sim_params.iteritems():
f_sim_params.attrs[attr] = value
f_network_params=f.create_group('network_params')
for attr, value in wta_params.iteritems():
f_network_params.attrs[attr] = value
f_pyr_params=f.create_group('pyr_params')
for attr, value in pyr_params.iteritems():
f_pyr_params.attrs[attr] = value
f_inh_params=f.create_group('inh_params')
for attr, value in inh_params.iteritems():
f_inh_params.attrs[attr] = value
for trial in range(sim_params.ntrials):
print('Trial %d' % trial)
vals[:,trial]=exp_rew
ev=vals[:,trial]*mags[:,trial]
inputs[0,trial]=ev[0]
inputs[1,trial]=ev[1]
inputs[:,trial]=40.0+40.0*inputs[:,trial]
trial_monitor=run_wta(wta_params, inputs[:,trial], sim_params, record_lfp=False, record_voxel=False,
record_neuron_state=False, record_spikes=True, record_firing_rate=True, record_inputs=False,
plot_output=False)
e_rates = []
for i in range(wta_params.num_groups):
e_rates.append(trial_monitor.monitors['excitatory_rate_%d' % i].smooth_rate(width=5 * ms, filter='gaussian'))
i_rates = [trial_monitor.monitors['inhibitory_rate'].smooth_rate(width=5 * ms, filter='gaussian')]
if output_file is not None:
trial_group=f.create_group('trial %d' % trial)
trial_group['e_rates'] = np.array(e_rates)
trial_group['i_rates'] = np.array(i_rates)
rt,decision_idx=get_response_time(e_rates, sim_params.stim_start_time, sim_params.stim_end_time,
upper_threshold=wta_params.resp_threshold, lower_threshold=None, dt=sim_params.dt)
reward=0.0
if decision_idx>=0 and np.random.random()<=prob_walk[decision_idx,trial]:
reward=1.0
exp_rew[decision_idx]=(1.0-alpha)*exp_rew[decision_idx]+alpha*reward
choice[trial]=decision_idx
rts[trial]=rt
rew[trial]=reward
param_ests,prop_correct=fit_behavior(prob_walk, mags, rew, choice)
if output_file is not None:
f.attrs['est_alpha']=param_ests[0]
f.attrs['est_beta']=param_ests[1]
f.attrs['prop_correct']=prop_correct
f['prob_walk']=prob_walk
f['mags']=mags
f['rew']=rew
f['choice']=choice
f['vals']=vals
f['inputs']=inputs
f['rts']=rts
f.close()
def plot_network_firing_rates(e_rates,
sim_params,
network_params,
std_e_rates=None,
i_rate=None,
std_i_rate=None,
plt_title=None,
labels=None,
ax=None):
rt, choice = get_response_time(
e_rates,
sim_params.stim_start_time,
sim_params.stim_end_time,
upper_threshold=network_params.resp_threshold,
dt=sim_params.dt)
if ax is None:
figure()
max_rates = [network_params.resp_threshold]
if i_rate is not None:
max_rates.append(np.max(i_rate[500:]))
for i in range(network_params.num_groups):
max_rates.append(np.max(e_rates[i, 500:]))
max_rate = np.max(max_rates)
if i_rate is not None:
ax = subplot(211)
elif ax is None:
ax = subplot(111)
rect = Rectangle(
(0, 0), (sim_params.stim_end_time - sim_params.stim_start_time) / ms,
max_rate + 5,
alpha=0.25,
facecolor='yellow',
edgecolor='none')
ax.add_patch(rect)
for idx in range(network_params.num_groups):
label = 'e %d' % idx
if labels is not None:
label = labels[idx]
time_ticks = (np.array(range(e_rates.shape[1])) *
sim_params.dt) / ms - sim_params.stim_start_time / ms
baseline, = ax.plot(time_ticks, e_rates[idx, :], label=label)
if std_e_rates is not None:
ax.fill_between(time_ticks,
e_rates[idx, :] - std_e_rates[idx, :],
e_rates[idx, :] + std_e_rates[idx, :],
alpha=0.5,
facecolor=baseline.get_color())
ylim(0, max_rate + 5)
ax.plot([
0 - sim_params.stim_start_time / ms,
(sim_params.trial_duration - sim_params.stim_start_time) / ms
], [
network_params.resp_threshold / hertz,
network_params.resp_threshold / hertz
], 'k--')
ax.plot([rt, rt], [0, max_rate + 5], 'k--')
legend(loc='best')
ylabel('Firing rate (Hz)')
if plt_title is not None:
title(plt_title)
if i_rate is not None:
ax = subplot(212)
rect = Rectangle(
(0, 0),
(sim_params.stim_end_time - sim_params.stim_start_time) / ms,
max_rate + 5,
alpha=0.25,
facecolor='yellow',
edgecolor='none')
ax.add_patch(rect)
label = 'i'
if labels is not None:
label = labels[network_params.num_groups]
time_ticks = (np.array(range(len(i_rate))) *
sim_params.dt) / ms - sim_params.stim_start_time / ms
baseline, = ax.plot(time_ticks, i_rate, label=label)
if std_i_rate is not None:
ax.fill_between(time_ticks,
i_rate - std_i_rate,
i_rate + std_i_rate,
alpha=0.5,
facecolor=baseline.get_color())
ylim(0, max_rate + 5)
ax.plot([rt, rt], [0, max_rate], 'k--')
ylabel('Firing rate (Hz)')
xlabel('Time (ms)')
