A general method to generate artificial spike train populations matching recorded neurons.

ABSTRACT: We developed a general method to generate populations of artificial spike trains (ASTs) that match the statistics of recorded neurons. The method is based on computing a Gaussian local rate function of the recorded spike trains, which results in rate templates from which ASTs are drawn as gamma distributed processes with a refractory period. Multiple instances of spike trains can be sampled from the same rate templates. Importantly, we can manipulate rate-covariances between spike trains by performing simple algorithmic transformations on the rate templates, such as filtering or amplifying specific frequency bands, and adding behavior related rate modulations. The method was examined for accuracy and limitations using surrogate data such as sine wave rate templates, and was then verified f

SUBMITTER: Abbasi S 

PROVIDER: S-EPMC7036336 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature