Spike train generation

Functions to generate/extract spike trains from analog signals, or to generate random spike trains.

Extract spike times from time series

spike_extraction(signal[, threshold, sign, ...])	Return the peak times for all events that cross threshold and the waveforms.
threshold_detection(signal[, threshold, sign])	Returns the times when the analog signal crosses a threshold.
peak_detection(signal[, threshold, sign, ...])	Return the peak times for all events that cross threshold.

Random spike train processes

StationaryPoissonProcess(rate[, t_start, ...])	Generates spike trains whose spikes are realizations of a stationary Poisson process with the given rate, starting at time t_start and stopping at time t_stop (Deger et al., 2012).
StationaryGammaProcess(rate, shape_factor[, ...])	Generates spike trains whose spikes are realizations of a stationary Gamma process with the given rate and shape_factor starting at time t_start and stopping at time t_stop.
StationaryLogNormalProcess(rate, sigma[, ...])	Generates spike trains whose spikes are realizations of a stationary LogNormal process with the given rate and sigma starting at time t_start and stopping at time t_stop.
StationaryInverseGaussianProcess(rate, cv[, ...])	Generates spike trains whose spikes are realizations of a stationary Gamma process with the given rate and cv starting at time t_start and stopping at time t_stop.
NonStationaryPoissonProcess(rate_signal[, ...])	Generates spike trains whose spikes are realizations of a non-stationary Poisson process with the given rate-signal.
NonStationaryGammaProcess(rate_signal, ...)	Generates spike trains whose spikes are realizations of a non-stationary Gamma process with the given rate-signal.

Coincident spike times generation

single_interaction_process(rate, ...[, ...])	Generates a multidimensional Poisson SIP (single interaction process) plus independent Poisson processes (Kuhn et al., 2003).
compound_poisson_process(rate, ...[, shift, ...])	Generate a Compound Poisson Process (CPP; see (Staude et al., 2010)) with a given amplitude_distribution \(A\) and stationary marginal rates rate.

Some functions are based on the NeuroTools stgen module, which was mostly written by Eilif Muller, or from the NeuroTools signals.analogs module.

References

[Deger, 2012] (1,2,3)

Moritz Deger, Moritz Helias, Clemens Boucsein, and Stefan Rotter. Statistical properties of superimposed stationary spike trains. Journal of Computational Neuroscience, 32(3):443–463, 2012.

[Kuhn, 2003] (1,2)

Alexandre Kuhn, Ad Aertsen, and Stefan Rotter. Higher-order statistics of input ensembles and the response of simple model neurons. Neural computation, 15(1):67–101, 2003.

[Staude, 2010] (1,2)

Benjamin Staude, Stefan Rotter, and Sonja Grün. Cubic: cumulant based inference of higher-order correlations in massively parallel spike trains. Journal of computational neuroscience, 29(1-2):327–350, 2010.