{"id":350,"date":"2012-06-07T16:59:14","date_gmt":"2012-06-07T14:59:14","guid":{"rendered":"http:\/\/project.inria.fr\/keops\/?page_id=350"},"modified":"2012-07-24T17:17:31","modified_gmt":"2012-07-24T15:17:31","slug":"recherche-analyser","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/keops\/recherche-2\/recherche-analyser\/","title":{"rendered":"Research : Analysing"},"content":{"rendered":"

Statistical analysis of retinal neural coding response: A framework from statistical physics.<\/span><\/strong><\/span><\/p>\n

Objective:<\/strong> Analyze, at the local network level, the statistical properties of ganglions cells retinal\u00a0output spike trains thus including adaptation mechanisms.<\/p>\n

Methods:<\/strong> Recent advances in multi-electrodes recording have thus brought us closer to\u00a0understanding how populations of retinal ganglion cells encode visual information. By monitoring\u00a0the visual responses of many ganglion cells at once, it is now possible to examine how ganglion\u00a0cells act together to encode a visual scene. To attain this objective, a quantitative and statistical\u00a0analysis of the ganglion cells spiking activity is required.
\nThis issue is faced to the delicate problem of proposing and validating accurate statistical model\u00a0fitting the empirical spike trains. It has been shown in (Schneidman et al, 2006; Cessac et al,\u00a02009; Cessac, 2010) that Gibbs measures constitute optimal parametric models, the estimated\u00a0Gibbs potential allowing to produce population rate, correlations or synchronization pattern,\u00a0providing an effective statistical tool.
\nSince, using the Gibbs potential framework allows us to obtain parametric estimations of spike\u00a0train observables, e.g. the population rate, correlations, or synchronization pattern occurrence\u00a0probability, this statistical tool appears to be a very interesting way of attaining our objective,\u00a0allowing us to relate the observed spiking activity to higher scales of observation of the neuronal\u00a0activity. For instance, the population spiking rate and correlation, or even higher order statistics\u00a0can be measured using the previous parametric model and then integrated in mean-field\u00a0mesoscopic models (Faugeras et al, 2009).
\nWe propose to apply an open-source library (EnaS) which estimates a polynomial Gibbs potential\u00a0over population spike trains and subsequently the population firing rate, correlations, higher order\u00a0statistics and relative entropy (Vasquez et al, 2010). The software module EnaS has been already validated at the programmatic level (functional tests).
\nTwo types of population spike trains will be studied:
\n– Simulated spike-trains of well-defined statistics in order to evaluate the quality and precision\u00a0of the method and,
\n– Experimental spike-trains provided by CINV in order to evaluate the pertinence and the\u00a0applicability of the method at the biological level.<\/p>\n

 <\/p>\n

The available framework allows\u00a0the comparison of different statistical models, which is a precious tool to disambiguate the correlations origin. This is illustrated in the following figure where a 2nd order Gibbs-distribution (bgibbs-2) is estimated considering several models, the correct model being easy to detect as the \u201c1st unbiased estimation\u201d (after Vasquez et al, 2010, where a rigorous formalism is proposed).<\/p>\n

\"\"<\/a>
\nTask steps:<\/strong>
\n(i) Artificial spikes-train benchmarks, using statistics as observed in retinal cells.
\n(ii) Simulated spikes-train benchmarks, as produced by the retinal simulator
\n(iii) Experimental spike-trains benchmarks, using data from Task1 after \u201cspike-sorting\u201d preprocessing.<\/p>\n


\n<\/strong><\/p>","protected":false},"excerpt":{"rendered":"

Statistical analysis of retinal neural coding response: A framework from statistical physics. Objective: Analyze, at the local network level, the statistical properties of ganglions cells retinal\u00a0output spike trains thus including adaptation mechanisms. Methods: Recent advances in multi-electrodes recording have thus brought us closer to\u00a0understanding how populations of retinal ganglion cells encode visual information. By monitoring\u00a0the … <\/p>\n

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