Decoding the retina with the first wave of spikes

Geoffrey Portelli 1, * John M. Barrett 2 Evelyne Sernagor 2 Pierre Kornprobst 1
* Corresponding author
1 NEUROMATHCOMP - Mathematical and Computational Neuroscience
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
Abstract : Understanding how the retina encodes visual information remains an open question. Using MEAs on salamander retinas, Gollisch & Meister (2008) showed that the relative latencies between some neuron pairs carry sufficient information to identify the phase of square-wave gratings (,. Using gratings of varying phase, spatial frequency, and contrast on mouse retinas, we extended this idea by systematically considering the relative order of all spike latencies, i.e. the shape of the first wave of spikes after stimulus onset. The discrimination task was to identify the phase among gratings of identical spatial frequency. We compared the performance (fraction correct predictions) of our approach under classical Bayesian and LDA decoders to spike count and response latency of each recorded neuron. Best results were obtained for the lowest spatial frequency. There, results showed that the spike count discrimination performance was higher than for latency under both the Bayesian (0,95+-0,02 and 0,75+-0,11 respectively) and LDA (0,95+-0,01 and 0,62+-0,03 respectively) decoders. The first wave of spikes decoder is (0,46+-0,06) less efficient than the spike count. Nevertheless, it accounts for 50% of the overall performance. Interestingly, these results tend to confirm the rank order coding hypothesis (Thorpe & Gautrais, 1998).
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Poster communications
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https://hal.inria.fr/hal-00920543
Contributor : Pierre Kornprobst <>
Submitted on : Wednesday, December 18, 2013 - 4:34:41 PM
Last modification on : Thursday, May 3, 2018 - 1:32:58 PM
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Geoffrey Portelli, John M. Barrett, Evelyne Sernagor, Pierre Kornprobst. Decoding the retina with the first wave of spikes. European Retina Meeting - 2013, Oct 2013, Alicante, Spain. 2013. ⟨hal-00920543⟩

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