if SAMPLES[drawn_samples[trial_num]][0] == 1:
spike_times = np.array(static_spikes_arr[neuron_idx])
spike_times += time_elapsed
neuron_poisson_spikes = np.hstack((neuron_poisson_spikes, spike_times))
else:
if SAMPLES[drawn_samples[trial_num]][1] == 1:
spike_times = np.array(static_spikes_arr[neuron_idx])
spike_times += time_elapsed
neuron_poisson_spikes = np.hstack((neuron_poisson_spikes, spike_times))
time_elapsed += STIMULUS_TIMESTEPS
wait_plus_iti = WAIT_TIMESTEPS + itis[trial_num]
spike_times = create_poisson_spikes(wait_plus_iti, WAIT_FREQ, SPIKE_DT, TIME_FACTOR)
spike_times += time_elapsed
neuron_poisson_spikes = np.hstack((neuron_poisson_spikes, spike_times))
time_elapsed += wait_plus_iti
poisson_spikes.append(neuron_poisson_spikes)
spike_counts = [len(n) for n in poisson_spikes]
end_spike = np.cumsum(spike_counts)
start_spike = np.empty_like(end_spike)
start_spike[0] = 0
start_spike[1:] = end_spike[0:-1]
spikeTimes = np.hstack(poisson_spikes).astype(float)
# """
"""
First we create the poisson spike trains for all the input neurons.
Below, `poisson_spikes` is a list of 100 lists: each list is the spike times for each neuron.
We create `start_spike` and `end_spike`, which we need to initialize the input layer.
`start_spike` and `end_spike` give the indices at which each neuron's spike times starts and ends
e.g. start_spike[0] is the starting index and end_spike[0] is the ending index of the 0th neuron's spike times.
"""
poisson_spikes = []
freq = 8
spike_dt = 0.001
N_INPUT = 100
interval = int(PRESENT_TIMESTEPS)
for p in range(N_INPUT):
neuron_spike_train = create_poisson_spikes(interval, freq, spike_dt, 1.0)
poisson_spikes.append(neuron_spike_train)
spike_counts = [len(n) for n in poisson_spikes]
end_spike = np.cumsum(spike_counts)
start_spike = np.empty_like(end_spike)
start_spike[0] = 0
start_spike[1:] = end_spike[0:-1]
spikeTimes = np.hstack(poisson_spikes).astype(float)
"""
The target spike train is a series of 5 equidistant spikes, which we create below.
"""
SUPERSPIKE_PARAMS["update_t"] = PRESENT_TIMESTEPS
TIME_FACTOR = 0.1
"""
First we create the poisson spike trains for all the input neurons.
Below, `poisson_spikes` is a list of 100 lists: each list is the spike times for each neuron.
We create `start_spike` and `end_spike`, which we need to initialize the input layer.
`start_spike` and `end_spike` give the indices at which each neuron's spike times starts and ends
e.g. start_spike[0] is the starting index and end_spike[0] is the ending index of the 0th neuron's spike times.
"""
poisson_spikes = []
freq = 8
spike_dt = 0.001
N_INPUT = 100
for p in range(N_INPUT):
neuron_spike_train = create_poisson_spikes(interval, freq, spike_dt,
TIME_FACTOR)
poisson_spikes.append(neuron_spike_train)
spike_counts = [len(n) for n in poisson_spikes]
end_spike = np.cumsum(spike_counts)
start_spike = np.empty_like(end_spike)
start_spike[0] = 0
start_spike[1:] = end_spike[0:-1]
spikeTimes = np.hstack(poisson_spikes).astype(float)
"""
The target spike train is a series of 5 equidistant spikes, which we create below.
"""
base_target_spike_times = np.linspace(0, 500, num=7)[1:6].astype(int)
target_spike_counts = [len(n) for n in target_poisson_spikes]
target_end_spike = np.cumsum(target_spike_counts)
target_start_spike = np.empty_like(target_end_spike)
target_start_spike[0] = 0
target_start_spike[1:] = target_end_spike[0:-1]
target_spikeTimes = np.hstack(target_poisson_spikes).astype(float)
"""
We also create the spike trains we need for the
repeating Poisson input noise.
"""
poisson_spikes = []
for neuron_idx in range(N_INPUT):
spike_times = create_poisson_spikes(TIMESTEPS, INPUT_FREQ, spike_dt,
TIME_FACTOR)
time_elapsed = 0
neuron_poisson_spikes = np.empty(0)
for trial_idx in range(TRIALS):
neuron_poisson_spikes = np.hstack((neuron_poisson_spikes, spike_times))
spike_times += TIMESTEPS
poisson_spikes.append(neuron_poisson_spikes)
spike_counts = [len(n) for n in poisson_spikes]
end_spike = np.cumsum(spike_counts)
start_spike = np.empty_like(end_spike)
start_spike[0] = 0
start_spike[1:] = end_spike[0:-1]
spikeTimes = np.hstack(poisson_spikes).astype(float)
"""