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Inferring the function performed by a recurrent neural network

View ORCID ProfileMatthew Chalk, View ORCID ProfileGašper Tkačik, View ORCID ProfileOlivier Marre
doi: https://doi.org/10.1101/598086
Matthew Chalk
1Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France
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  • For correspondence: matthewjchalk@gmail.com
Gašper Tkačik
2IST Austria, A-3400, Klosterneuburg, Austria
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Olivier Marre
1Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France
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Abstract

A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. Here, we propose an alternative approach that uses recorded neural responses to directly infer the function performed by a neural network. We assume that the goal of the network can be expressed via a reward function, which describes how desirable each state of the network is for carrying out a given objective. This allows us to frame the problem of optimising each neuron’s responses by viewing neurons as agents in a reinforcement learning (RL) paradigm; likewise the problem of inferring the reward function from the observed dynamics can be treated using inverse RL. Our framework encompasses previous influential theories of neural coding, such as efficient coding and attractor network models, as special cases, given specific choices of reward function. Finally, we can use the reward function inferred from recorded neural responses to make testable predictions about how the network dynamics will adapt depending on contextual changes, such as cell death and/or varying input statistics, so as to carry out the same underlying function with different constraints.

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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. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 05, 2019.
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Inferring the function performed by a recurrent neural network
Matthew Chalk, Gašper Tkačik, Olivier Marre
bioRxiv 598086; doi: https://doi.org/10.1101/598086
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Inferring the function performed by a recurrent neural network
Matthew Chalk, Gašper Tkačik, Olivier Marre
bioRxiv 598086; doi: https://doi.org/10.1101/598086

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