Statistical models for spike trains analysis in the retina.

Description

Recent advances in multi-electrodes array acquisition have made it possible to record the activity ofup to several hundreds of neurons at the same time and to register their collective activity (spiketrains). For the retina, this opens up new perspectives in understanding how retinal structure and ganglion cells encode information about a visual sceneand what is transmitted to the brain. Especially, two paradigms can be confronted: in the first one, ganglion cells encodeinformation independently of each others; in the second one non linear dynamics and connectivity contributeto produce a population coding where spatio-temporal correlations, although weak, play a significant role inspike 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 contributeto 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 presentour main results in this talk.

Abstract

International audience

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