timescales.sim.sim_spikes_synaptic

timescales.sim.sim_spikes_synaptic(n_seconds, fs, tau, mu=None, refract=None, isi=None, var_noise=None)

Simulate a spiking autocorrelation as a synaptic kernel.

Parameters

n_secondsfloat

Length of the signal, in seconds.

fsfloat

Sampling rate, in hz.

taufloat

Timescale, in seconds.

mufloat, optional, default: None

Mean of the isi exponential distribuion. Only used if isi is None.

refractint, optional, default: None

Minimum distances between spikes (i.e. refactory period).

isi1d array, optional, default: None

Interspike intervals to randomly sample from.

var_noisefloat, optional, default: None

Variance of gaussian noise to be added to spike probabilities. Larger values, approaching 1, will produce smaller spectral exponents.

return_sumbool, optional, default: True

Returns sum of neurons if True. If False, a 2d binary array is returned.

Returns

spikes1d

Spike counts.