Microsaccades enable efficient synchrony-based visual feature learning and detection

Timothée Masquelier 1, * Geoffrey Portelli 2 Pierre Kornprobst 2
* Corresponding author
2 NEUROMATHCOMP - Mathematical and Computational Neuroscience
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
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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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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