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Computing Complex Visual Features with Retinal Spike Times

Gütig, Robert and Gollisch, Tim and Sompolinsky, Haim and Meister, Markus (2013) Computing Complex Visual Features with Retinal Spike Times. PLoS ONE, 8 (1). Art. No. e53063. ISSN 1932-6203. PMCID PMC3534662. doi:10.1371/journal.pone.0053063.

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Neurons in sensory systems can represent information not only by their firing rate, but also by the precise timing of individual spikes. For example, certain retinal ganglion cells, first identified in the salamander, encode the spatial structure of a new image by their first-spike latencies. Here we explore how this temporal code can be used by downstream neural circuits for computing complex features of the image that are not available from the signals of individual ganglion cells. To this end, we feed the experimentally observed spike trains from a population of retinal ganglion cells to an integrate-and-fire model of post-synaptic integration. The synaptic weights of this integration are tuned according to the recently introduced tempotron learning rule. We find that this model neuron can perform complex visual detection tasks in a single synaptic stage that would require multiple stages for neurons operating instead on neural spike counts. Furthermore, the model computes rapidly, using only a single spike per afferent, and can signal its decision in turn by just a single spike. Extending these analyses to large ensembles of simulated retinal signals, we show that the model can detect the orientation of a visual pattern independent of its phase, an operation thought to be one of the primitives in early visual processing. We analyze how these computations work and compare the performance of this model to other schemes for reading out spike-timing information. These results demonstrate that the retina formats spatial information into temporal spike sequences in a way that favors computation in the time domain. Moreover, complex image analysis can be achieved already by a simple integrate-and-fire model neuron, emphasizing the power and plausibility of rapid neural computing with spike times.

Item Type:Article
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URLURL TypeDescription CentralArticle
Meister, Markus0000-0003-2136-6506
Additional Information:© 2013 Gütig et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received August 26, 2012; Accepted November 28, 2012; Published January 2, 2013. This research was supported by the Minerva Foundation and the German Research Foundation (RG); the Max Planck Society, the German Research Foundation SFB 889, and the International Human Frontier Science Program Organization (TG); the Israel Science Foundation, the Israeli Ministry of Defense, and the Gatsby Charitable Foundation (HS); and the National Institutes of Health (MM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contributions: Conceived and designed the experiments: RG TG HS MM. Performed the experiments: RG TG. Analyzed the data: RG TG. Wrote the paper: RG TG HS MM. The authors have declared that no competing interests exist.
Funding AgencyGrant Number
Minerva FoundationUNSPECIFIED
Deutsche Forschungsgemeinschaft (DFG)SFB 889
Max Planck SocietyUNSPECIFIED
Human Frontier Science ProgramUNSPECIFIED
Israel Science FoundationUNSPECIFIED
Ministry of Defense (Israel)UNSPECIFIED
Gatsby Charitable FoundationUNSPECIFIED
Issue or Number:1
PubMed Central ID:PMC3534662
Record Number:CaltechAUTHORS:20170405-103352974
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Official Citation:Gütig R, Gollisch T, Sompolinsky H, Meister M (2013) Computing Complex Visual Features with Retinal Spike Times. PLoS ONE 8(1): e53063. doi:10.1371/journal.pone.0053063
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:75733
Deposited By: Tony Diaz
Deposited On:05 Apr 2017 17:44
Last Modified:15 Nov 2021 16:36

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