Statistical models for spike trains analysis in the retina.

Bruno Cessac 1
1 NEUROMATHCOMP - Mathematical and Computational Neuroscience
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
Abstract : Recent advances in multi-electrodes array acquisition have made it possible to record the activity of up to several hundreds of neurons at the same time and to register their collective activity (spike trains). For the retina, this opens up new perspectives in understanding how retinal structure and ganglion cells encode information about a visual scene and what is transmitted to the brain. Especially, two paradigms can be confronted: in the first one, ganglion cells encode information independently of each others; in the second one non linear dynamics and connectivity contribute to produce a population coding where spatio-temporal correlations, although weak, play a significant role in spike coding. Confronting these two paradigms can be done at an experimental and at a theoretical level. On experimental grounds, new methods to analyse the role of weak correlations in spike train statistics are required. On theoretical grounds, mathematical results have been established, in neuronal models, showing how non linear dynamics and connectivity contribute to produce a correlated spike response to stimuli. In the context of the ANR KEOPS project we have been working on these two aspects and we shall present our main results in this talk.
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https://hal.inria.fr/hal-01235313
Contributeur : Bruno Cessac <>
Soumis le : jeudi 10 décembre 2015 - 13:45:30
Dernière modification le : jeudi 3 mai 2018 - 13:32:58

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  • HAL Id : hal-01235313, version 1

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Bruno Cessac. Statistical models for spike trains analysis in the retina.. 12eme Colloque de la société des neurosciences, May 2015, Montpellier, France. 〈hal-01235313〉

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