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Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons

Ulisse Ferrari, Stéphane Deny, Matthew Chalk, Gašper Tkačik, Olivier Marre, Thierry Mora
doi: https://doi.org/10.1101/243816
Ulisse Ferrari
1Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France.
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  • For correspondence: ulisse.ferrari@gmail.com
Stéphane Deny
2Neural Dynamics and Computation Lab, Stanford University, California
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Matthew Chalk
1Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France.
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Gašper Tkačik
3Institute of Science and Technology, Klosterneuburg, Austria
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Olivier Marre
1Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France.
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Thierry Mora
1Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France.
5Laboratoire de physique statistique, CNRS, Sorbonne Université, Université Paris-Diderot and École normale supérieure (PSL), 24, rue Lhomond, 75005 Paris, France
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Abstract

Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network, and thus depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes of the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer population models of interacting neurons that collectively encode stimulus information. The key to disentangling intrinsic from extrinsic correlations is to infer the couplings between neurons separately from the encoding model, and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach on retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus.

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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 February 22, 2018.
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Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons
Ulisse Ferrari, Stéphane Deny, Matthew Chalk, Gašper Tkačik, Olivier Marre, Thierry Mora
bioRxiv 243816; doi: https://doi.org/10.1101/243816
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Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons
Ulisse Ferrari, Stéphane Deny, Matthew Chalk, Gašper Tkačik, Olivier Marre, Thierry Mora
bioRxiv 243816; doi: https://doi.org/10.1101/243816

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