Spike train generation
Functions to generate/extract spike trains from analog signals, or to generate
random spike trains.
Random spike train processes
homogeneous_poisson_process (rate[, t_start, …]) |
Returns a spike train whose spikes are a realization of a Poisson process with the given rate, starting at time t_start and stopping time t_stop. |
inhomogeneous_poisson_process (rate[, …]) |
Returns a spike train whose spikes are a realization of an inhomogeneous Poisson process with the given rate profile. |
homogeneous_gamma_process (a, b[, t_start, …]) |
Returns a spike train whose spikes are a realization of a gamma process with the given parameters, starting at time t_start and stopping time t_stop (average rate will be b/a). |
inhomogeneous_gamma_process (rate, shape_factor) |
Returns a spike train whose spikes are a realization of an inhomogeneous Gamma process with the given rate profile and the given shape factor [gen2]. |
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 [gen1]) 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] | 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. |
[gen2] | Martin P Nawrot, Clemens Boucsein, Victor Rodriguez Molina, Alexa Riehle, Ad Aertsen, and Stefan Rotter. Measurement of variability dynamics in cortical spike trains. Journal of neuroscience methods, 169(2):374–390, 2008. |
[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. |