Rank order coding: a retinal information decoding strategy revealed by large-scale multielectrode array retinal recordings

Abstract : How a population of retinal ganglion cells (RGCs) encodes the visual scene remains an open question. Going beyond individual RGC coding strategies, results in salamander suggest that the relative latencies of an RGC pair encodes spatial information. Thus a population code based on this concerted spiking could be a powerful mechanism to transmit visual information rapidly and efficiently. Here, we tested this hypothesis in mouse by recording simultaneous light-evoked responses from hundreds of RGCs, at pan-retinal level, using a new generation of large-scale, high density multielectrode array consisting of 4096 electrodes. Interestingly, we did not find any RGCs exhibiting a clear latency tuning to the stimuli, suggesting that in mouse, individual RGC pairs may not provide sufficient information. We show that a significant amount of information is encoded synergistically in the concerted spiking of large RGC populations. Thus, the RGC population response described with relative activities, or ranks, provides more relevant information than classical independent spike count- or latency- based codes. In particular, we report for the first time that when considering the relative activities across the whole population, the wave of first stimulus-evoked spikes (WFS) is an accurate indicator of stimulus content. We show that this coding strategy co-exists with classical neural codes, and that it is more efficient and faster. Overall, these novel observations suggest that already at the level of the retina, concerted spiking provides a reliable and fast strategy to rapidly transmit new visual scenes.
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eNeuro, Society for Neuroscience, 2016, 〈10.1523/ENEURO.0134-15.2016〉
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Geoffrey Portelli, John M. Barrett, Gerrit Hilgen, Timothée Masquelier, Alessandro Maccione, et al.. Rank order coding: a retinal information decoding strategy revealed by large-scale multielectrode array retinal recordings. eNeuro, Society for Neuroscience, 2016, 〈10.1523/ENEURO.0134-15.2016〉. 〈hal-01316105〉

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