# Sample neuron subsets. The assumption is the PC axes of the RNN
# are not unit aligned, so sampling units is adequate to sample all
# the high-variance PCs.
P_sxn = np.eye(S,N)
for m in range(n):
P_sxn = np.roll(P_sxn, S, axis=1)
if input_magnitude > 0.0:
# time of "hits" randomly chosen between [1/4 and 3/4] of total time
input_times = rng.choice(int(ntime_steps/2), size=[E]) + int(ntime_steps/4)
else:
input_times = None
rates, x0s, inputs = \
generate_data(rnn, T=T, E=E, x0s=x0s, P_sxn=P_sxn,
input_magnitude=input_magnitude,
input_times=input_times)
if FLAGS.noise_type == "poisson":
noisy_data = spikify_data(rates, rng, rnn['dt'], rnn['max_firing_rate'])
elif FLAGS.noise_type == "gaussian":
noisy_data = gaussify_data(rates, rng, rnn['dt'], rnn['max_firing_rate'])
else:
raise ValueError("Only noise types supported are poisson or gaussian")
# split into train and validation sets
train_inds, valid_inds = get_train_n_valid_inds(E, train_percentage,
nreplications)
# Split the data, inputs, labels and times into train vs. validation.
rates_train, rates_valid = \
x0s = []
condition_labels = []
condition_number = 0
for c in range(C):
x0 = FLAGS.x0_std * rng.randn(N, 1)
x0s.append(np.tile(x0, nreplications))
for ns in range(nreplications):
condition_labels.append(condition_number)
condition_number += 1
x0s = np.concatenate(x0s, axis=1)
P_nxn = rng.randn(N, N) / np.sqrt(N)
# generate trials for both RNNs
rates_a, x0s_a, _ = generate_data(rnn_a, T=T, E=E, x0s=x0s, P_sxn=P_nxn,
input_magnitude=0.0, input_times=None)
spikes_a = spikify_data(rates_a, rng, rnn_a['dt'], rnn_a['max_firing_rate'])
rates_b, x0s_b, _ = generate_data(rnn_b, T=T, E=E, x0s=x0s, P_sxn=P_nxn,
input_magnitude=0.0, input_times=None)
spikes_b = spikify_data(rates_b, rng, rnn_b['dt'], rnn_b['max_firing_rate'])
# not the best way to do this but E is small enough
rates = []
spikes = []
for trial in xrange(E):
if rnn_to_use[trial] == 0:
rates.append(rates_a[trial])
spikes.append(spikes_a[trial])
else:
rates.append(rates_b[trial])
condition_number = 0
for c in range(C):
x0 = FLAGS.x0_std * rng.randn(N, 1)
x0s.append(np.tile(x0, nreplications))
for ns in range(nreplications):
condition_labels.append(condition_number)
condition_number += 1
x0s = np.concatenate(x0s, axis=1)
P_nxn = rng.randn(N, N) / np.sqrt(N)
# generate trials for both RNNs
rates_a, x0s_a, _ = generate_data(rnn_a,
T=T,
E=E,
x0s=x0s,
P_sxn=P_nxn,
input_magnitude=0.0,
input_times=None)
spikes_a = spikify_data(rates_a, rng, rnn_a['dt'], rnn_a['max_firing_rate'])
rates_b, x0s_b, _ = generate_data(rnn_b,
T=T,
E=E,
x0s=x0s,
P_sxn=P_nxn,
input_magnitude=0.0,
input_times=None)
spikes_b = spikify_data(rates_b, rng, rnn_b['dt'], rnn_b['max_firing_rate'])
# not the best way to do this but E is small enough
input_times = np.vstack(
(input_times,
(rng.choice(int(ntime_steps / 2), size=[E]) +
int(ntime_steps / 4))))
else:
input_times = rng.choice(int(ntime_steps / 2), size=[E]) + int(
ntime_steps / 4)
else:
input_times = None
print(ninputs)
if ninputs > 1:
rates, x0s, inputs = \
generate_data(rnn, T=T, E=E, x0s=x0s, P_sxn=P_sxn,
input_magnitude=input_magnitude_list,
input_times=input_times, ninputs=ninputs, rng=rng)
else:
rates, x0s, inputs = \
generate_data(rnn, T=T, E=E, x0s=x0s, P_sxn=P_sxn,
input_magnitude=input_magnitude,
input_times=input_times, ninputs=ninputs, rng=rng)
if FLAGS.noise_type == "poisson":
noisy_data = spikify_data(rates, rng, rnn['dt'],
rnn['max_firing_rate'])
elif FLAGS.noise_type == "gaussian":
noisy_data = gaussify_data(rates, rng, rnn['dt'],
rnn['max_firing_rate'])
else:
raise ValueError("Only noise types supported are poisson or gaussian")
