EnaS: a new software for neural population analysis in large scale spiking networks

Hassan Nasser 1, * Selim Kraria 2 Bruno Cessac 1
* Corresponding author
1 NEUROMATHCOMP - Mathematical and Computational Neuroscience
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
Abstract : With the advent of new Multi-Electrode Arrays techniques (MEA), the simultaneous recording of the activity up to hundreds of neurons over a dense configuration supplies today a critical database to unravel the role of specific neural assemblies. Thus, the analysis of spike trains obtained from in vivo or in vitro experimental data requires suitable statistical models and computational tools. The EnaS software, developed by our team, offers new computational methods of spike train statistics, based on Gibbs distributions (in its more general sense, including, but not limited, to the Maximal Entropy - MaxEnt) and taking into account time constraints in neural networks (such as memory effects). It also offers several statistical model choices, some of these models already used in the community (such as the conditional intensity models [5]), and some others developed by us ([1] and [2]), and allows a quantitative comparison between these models. It also offers a control of finite-size sampling effects inherent to empirical statistics.
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Hassan Nasser, Selim Kraria, Bruno Cessac. EnaS: a new software for neural population analysis in large scale spiking networks. Twenty Second Annual Computational Neuroscience Meeting : CNS 2013, Jul 2013, Paris, France. 14 (Suppl 1), pp.P57, 2013. ⟨hal-00842303⟩

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