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A Biologically Plausible Mechanism to Learn Clusters of Neural Activity

Adrianna R. Loback, Michael J. Berry II
doi: https://doi.org/10.1101/389155
Adrianna R. Loback
1Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
2Department of Engineering, University of Cambridge, Cambridge, UK
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Michael J. Berry II
1Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
3Molecular Biology Department, Princeton University, Princeton, NJ, USA
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Abstract

When correlations within a neural population are strong enough, neural activity in early visual areas is organized into a discrete set of clusters. Here, we show that a simple, biologically plausible circuit can learn and then readout in real-time the identity of experimentally measured clusters of retinal ganglion cell population activity. After learning, individual readout neurons develop cluster tuning, meaning that they respond strongly to any neural activity pattern in one cluster and weakly to all other inputs. Different readout neurons specialize for different clusters, and all input clusters can be learned, as long as the number of readout units is mildly larger than the number of input clusters. We argue that this operation can be repeated as signals flow up the cortical hierarchy.

Footnotes

  • ↵* arl64{at}cam.ac.uk

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted August 10, 2018.
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A Biologically Plausible Mechanism to Learn Clusters of Neural Activity
Adrianna R. Loback, Michael J. Berry II
bioRxiv 389155; doi: https://doi.org/10.1101/389155
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A Biologically Plausible Mechanism to Learn Clusters of Neural Activity
Adrianna R. Loback, Michael J. Berry II
bioRxiv 389155; doi: https://doi.org/10.1101/389155

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