Zylberberg, J;
Pouget, A;
Latham, PE;
Shea-Brown, E;
(2017)
Robust information propagation through noisy neural circuits.
PLoS Computational Biology
, 13
(4)
, Article e1005497. 10.1371/journal.pcbi.1005497.
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Abstract
Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural responses is not the only relevant feature of population codes; of potentially equal importance is how robustly that information propagates to downstream structures. For instance, to quantify the retina’s performance, one must consider not only the informativeness of the optic nerve responses, but also the amount of information that survives the spike-generating nonlinearity and noise corruption in the next stage of processing, the lateral geniculate nucleus. Our study identifies the set of covariance structures for the upstream cells that optimize the ability of information to propagate through noisy, nonlinear circuits. Within this optimal family are covariances with “differential correlations”, which are known to reduce the information encoded in neural population activities. Thus, covariance structures that maximize information in neural population codes, and those that maximize the ability of this information to propagate, can be very different. Moreover, redundancy is neither necessary nor sufficient to make population codes robust against corruption by noise: redundant codes can be very fragile, and synergistic codes can—in some cases—optimize robustness against noise.
| Type: | Article |
|---|---|
| Title: | Robust information propagation through noisy neural circuits |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pcbi.1005497 |
| Publisher version: | http://doi.org/10.1371/journal.pcbi.1005497 |
| Language: | English |
| Additional information: | © 2017 Zylberberg 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. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Biochemical Research Methods, Mathematical & Computational Biology, Biochemistry & Molecular Biology, PRIMARY VISUAL-CORTEX, HIGHER-ORDER INTERACTIONS, CODING EFFICIENCY, POPULATION CODE, SPIKING NEURONS, VARIABILITY, ORIENTATION, MECHANISM, INFERENCE, ACCURACY |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1552763 |
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