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Author Esheabrown, Eric ♦ Ehu, Yu ♦ Etrousdale, James ♦ Ejosić, Krešimir
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher Frontiers Media S.A.
File Format HTM / HTML
Date Created 2014-05-22
Copyright Year ©2013
Language English
Subject Domain (in LCC) RC321-571
Subject Keyword Neuropsychiatry ♦ Biological psychiatry ♦ Neurosciences ♦ Neuronal networks ♦ Neuronal network model ♦ Point Processes ♦ Internal medicine ♦ Medicine ♦ Spiking neurons ♦ Correlations ♦ Neuronal modeling
Abstract Emerging technologies are revealing the spiking activity in ever larger neural ensembles. Frequently, this spiking is far from independent, with correlations in the spike times of different cells. Understanding how such correlations impact the dynamics and function of neural ensembles remains an important open problem.Here we describe a new, generative model for correlated spike trains that can exhibit many of the features observed in data. Extending prior work in mathematical finance, this generalized thinning and shift (GTaS) model creates marginally Poisson spike trains with diverse temporal correlation structures.We give several examples which highlight the model's flexibility and utility. For instance, we use it to examine how a neural network responds to highly structured patterns of inputs.We then show that the GTaS model is analytically tractable, and derive cumulant densities of all orders in terms of model parameters. The GTaS framework can therefore be an important tool in the experimental and theoretical exploration of neural dynamics.
ISSN 16625188
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2013-07-01
e-ISSN 16625188
Journal Frontiers in Computational Neuroscience
Volume Number 7


Source: Directory of Open Access Journals (DOAJ)