Spike train generation
Functions to generate/extract spike trains from analog signals, or to generate
random spike trains.
Random spike train processes
StationaryPoissonProcess (rate[, t_stop, ...]) |
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. |
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 [gen3]. |
compound_poisson_process (rate, ...[, shift, ...]) |
Generate a Compound Poisson Process (CPP; see [gen2]) with a given amplitude_distribution 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
[gen1] | 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. |
[gen2] | 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. |
[gen3] | 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. |