{"id":422,"date":"2012-06-14T13:01:42","date_gmt":"2012-06-14T11:01:42","guid":{"rendered":"http:\/\/project.inria.fr\/keops\/?page_id=422"},"modified":"2012-07-24T17:17:48","modified_gmt":"2012-07-24T15:17:48","slug":"outcomes-observing","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/keops\/outcomes\/outcomes-observing\/","title":{"rendered":"Outcomes : Observing"},"content":{"rendered":"

Principal investigator : Adrian PALACIOS<\/a>.<\/strong><\/p>\n

\"\"<\/a><\/strong><\/p>\n

On going work :<\/strong> Hardware and Software design of a new recording set-up to obtain spikes responses synchronized with natural stimulus presentation.<\/p>\n

[A] Build an Images sequences generator
\n[B] Build an OLED programing device for stimuli presentation
\n[C] Implementation of a Spike Sorting Algorithm (in collaboration with O. Marre)<\/p>\n

Collect existing and new experimental facts\u00a0on standard and no-standard behaviors of\u00a0 retinal\u00a0ganglions cells in order to understand the\u00a0computational capabilities of the retina under natural\u00a0scenes.
\n\u2013 Visual stimulator development :\u00a0 Artificial + Natural stimuli
\n\u2013 Natural image stimuli preparation
\n\u2013 Spike-sorting implementation and benchmarking
\n\u2013 Receptive field computation and benchmarking<\/p>\n

Publications:<\/strong><\/p>\n

Spike train statistics from empirical facts to theory:\u00a0the case of the retina, (B. Cessac and A. Palacios.) In Mathematical Problems in Computational Biology and Biomedicine, F. Cazals\u00a0and P. Kornprobst editors, Springer, submitted.<\/a><\/p>\n

Abstract: This chapter focuses on methods from statistical physics and\u00a0probability theory allowing the analysis of spike trains in neural networks. Taking\u00a0as an example the retina we present recent works attempting to understand how\u00a0retina ganglion cells encode the information transmitted to the visual cortex via\u00a0the optical nerve, by analyzing their spike train statistics. We compare the\u00a0maximal entropy models used in the literature of retina spike train analysis to\u00a0rigorous results establishing the exact form of spike train statistics in\u00a0conductance-based Integrate-and-Fire neural networks.<\/p>\n


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

Principal investigator : Adrian PALACIOS. On going work : Hardware and Software design of a new recording set-up to obtain spikes responses synchronized with natural stimulus presentation. [A] Build an Images sequences generator [B] Build an OLED programing device for stimuli presentation [C] Implementation of a Spike Sorting Algorithm (in collaboration with O. Marre) Collect … <\/p>\n

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