Literature DB >> 19431282

Generation of correlated spike trains.

Romain Brette1.   

Abstract

Neuronal spike trains display correlations at diverse timescales throughout the nervous system. The functional significance of these correlations is largely unknown, and computational investigations can help us understand their role. In order to generate correlated spike trains with given statistics, several case-specific methods have been described in the litterature. This letter presents two general methods to generate sets of spike trains with given firing rates and pairwise correlation functions, along with efficient simulation algorithms.

Mesh:

Year:  2009        PMID: 19431282     DOI: 10.1162/neco.2008.12-07-657

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  27 in total

Review 1.  Data-driven significance estimation for precise spike correlation.

Authors:  Sonja Grün
Journal:  J Neurophysiol       Date:  2009-01-07       Impact factor: 2.714

2.  A generative spike train model with time-structured higher order correlations.

Authors:  James Trousdale; Yu Hu; Eric Shea-Brown; Krešimir Josić
Journal:  Front Comput Neurosci       Date:  2013-07-17       Impact factor: 2.380

3.  Differences in intrinsic functional organization between dorsolateral prefrontal and posterior parietal cortex.

Authors:  Fumi Katsuki; Xue-Lian Qi; Travis Meyer; Phillip M Kostelic; Emilio Salinas; Christos Constantinidis
Journal:  Cereb Cortex       Date:  2013-03-31       Impact factor: 5.357

4.  Cortically-controlled population stochastic facilitation as a plausible substrate for guiding sensory transfer across the thalamic gateway.

Authors:  Sébastien Béhuret; Charlotte Deleuze; Leonel Gomez; Yves Frégnac; Thierry Bal
Journal:  PLoS Comput Biol       Date:  2013-12-26       Impact factor: 4.475

5.  Correlation-based analysis and generation of multiple spike trains using hawkes models with an exogenous input.

Authors:  Michael Krumin; Inna Reutsky; Shy Shoham
Journal:  Front Comput Neurosci       Date:  2010-11-19       Impact factor: 2.380

6.  Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise Correlations.

Authors:  Dmitry R Lyamzin; Jakob H Macke; Nicholas A Lesica
Journal:  Front Comput Neurosci       Date:  2010-11-15       Impact factor: 2.380

7.  A new method to infer higher-order spike correlations from membrane potentials.

Authors:  Imke C G Reimer; Benjamin Staude; Clemens Boucsein; Stefan Rotter
Journal:  J Comput Neurosci       Date:  2013-03-10       Impact factor: 1.621

8.  CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains.

Authors:  Benjamin Staude; Stefan Rotter; Sonja Grün
Journal:  J Comput Neurosci       Date:  2009-10-28       Impact factor: 1.621

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

Authors:  Samira Abbasi; Selva Maran; Dieter Jaeger
Journal:  J Comput Neurosci       Date:  2020-01-23       Impact factor: 1.621

10.  Spectral analysis of input spike trains by spike-timing-dependent plasticity.

Authors:  Matthieu Gilson; Tomoki Fukai; Anthony N Burkitt
Journal:  PLoS Comput Biol       Date:  2012-07-05       Impact factor: 4.475

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