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An Algorithmic Barrier to Neural Circuit Understanding

Venkatakrishnan Ramaswamy
doi: https://doi.org/10.1101/639724
Venkatakrishnan Ramaswamy
1Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK, Bellary Road, Bangalore 560065, India.
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  • For correspondence: vramaswamy@ncbs.res.in
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Abstract

Neuroscience is witnessing extraordinary progress in experimental techniques, especially at the neural circuit level. These advances are largely aimed at enabling us to understand how neural circuit computations mechanistically cause behavior. Here, using techniques from Theoretical Computer Science, we examine how many experiments are needed to obtain such an empirical understanding. It is proved, mathematically, that establishing the most extensive notions of understanding need exponentially-many experiments in the number of neurons, in general, unless a widely-posited hypothesis about computation is false. Worse still, the feasible experimental regime is one where the number of experiments scales sub-linearly in the number of neurons, suggesting a fundamental impediment to such an understanding. Determining which notions of understanding are algorithmically tractable, thus, becomes an important new endeavor in Neuroscience.

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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 May 26, 2019.
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An Algorithmic Barrier to Neural Circuit Understanding
Venkatakrishnan Ramaswamy
bioRxiv 639724; doi: https://doi.org/10.1101/639724
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An Algorithmic Barrier to Neural Circuit Understanding
Venkatakrishnan Ramaswamy
bioRxiv 639724; doi: https://doi.org/10.1101/639724

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