Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/46082
Title: Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina.
Authors: Liu, Jian K.
Gollisch, Tim
First Published: 31-Jul-2015
Publisher: Public Library of Science for International Society for Computational Biology (ISCB)
Citation: PLoS Computational Biology, 2015, 11(7): e100442
Abstract: When visual contrast changes, retinal ganglion cells adapt by adjusting their sensitivity as well as their temporal filtering characteristics. The latter has classically been described by contrast-induced gain changes that depend on temporal frequency. Here, we explored a new perspective on contrast-induced changes in temporal filtering by using spike-triggered covariance analysis to extract multiple parallel temporal filters for individual ganglion cells. Based on multielectrode-array recordings from ganglion cells in the isolated salamander retina, we found that contrast adaptation of temporal filtering can largely be captured by contrast-invariant sets of filters with contrast-dependent weights. Moreover, differences among the ganglion cells in the filter sets and their contrast-dependent contributions allowed us to phenomenologically distinguish three types of filter changes. The first type is characterized by newly emerging features at higher contrast, which can be reproduced by computational models that contain response-triggered gain-control mechanisms. The second type follows from stronger adaptation in the Off pathway as compared to the On pathway in On-Off-type ganglion cells. Finally, we found that, in a subset of neurons, contrast-induced filter changes are governed by particularly strong spike-timing dynamics, in particular by pronounced stimulus-dependent latency shifts that can be observed in these cells. Together, our results show that the contrast dependence of temporal filtering in retinal ganglion cells has a multifaceted phenomenology and that a multi-filter analysis can provide a useful basis for capturing the underlying signal-processing dynamics.
DOI Link: 10.1371/journal.pcbi.1004425
eISSN: 1553-7358
Links: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004425
http://hdl.handle.net/2381/46082
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2015. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Description: Data are available from the Dryad Digital Repository (doi: 10.5061/dryad.7r7n7).
Appears in Collections:Published Articles, Dept. of Neuroscience, Psychology and Behaviour

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