Microsaccades enable efficient synchrony-based visual feature learning and detection - Inria - Institut national de recherche en sciences et technologies du numérique
Poster Communications Year : 2014

Microsaccades enable efficient synchrony-based visual feature learning and detection

Abstract

Fixational eye movements are common across vertebrates, yet their functional roles, if any, are debated. To investigate this issue, we exposed the Virtual Retina simulator to natural images, generated realistic drifts and microsaccades using the model of ref, and analyzed the output spike trains of the parvocellular retinal ganglion cells (RGC).

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Neurons and Cognition [q-bio.NC]
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https://inria.hal.science/hal-01026508

Submitted on : Monday, July 21, 2014-5:04:04 PM

Last modification on : Thursday, May 2, 2024-2:25:48 PM

Long-term archiving on : Monday, November 24, 2014-9:27:52 PM

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hal-01026508 , version 1 (21-07-2014)

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  • HAL Id : hal-01026508 , version 1

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Timothée Masquelier, Geoffrey Portelli, Pierre Kornprobst. Microsaccades enable efficient synchrony-based visual feature learning and detection. Twenty Third Annual Computational Neuroscience Meeting: CNS*2014, Jul 2014, Québec, Canada. 15 (Suppl 1), pp.P121, 2014, BMC Neuroscience. ⟨hal-01026508⟩

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