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Learning probabilistic representations with randomly connected neural circuits

Ori Maoz, View ORCID ProfileGašper Tkacčik, Mohamad Saleh Esteki, Roozbeh Kiani, Elad Schneidman
doi: https://doi.org/10.1101/478545
Ori Maoz
1Department of Neurobiology
2Department of Computer Science, Weizmann Institute of Science, Rehovot 76100, Israel
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Gašper Tkacčik
3Institute of Science and Technology, A-3400 Klosterneuburg, Austria
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Mohamad Saleh Esteki
4Center for Neural Science
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Roozbeh Kiani
4Center for Neural Science
5Department of Psychology, New York University, New York, NY 10003, USA
6Neuroscience Institute, NYU Langone Medical Center, New York, NY 10016, USA
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Elad Schneidman
1Department of Neurobiology
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Abstract

The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a new model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficiently learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable or better than that of current models. Importantly, the model can be learned using a small number of samples, and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation.

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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 November 27, 2018.
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Learning probabilistic representations with randomly connected neural circuits
Ori Maoz, Gašper Tkacčik, Mohamad Saleh Esteki, Roozbeh Kiani, Elad Schneidman
bioRxiv 478545; doi: https://doi.org/10.1101/478545
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Learning probabilistic representations with randomly connected neural circuits
Ori Maoz, Gašper Tkacčik, Mohamad Saleh Esteki, Roozbeh Kiani, Elad Schneidman
bioRxiv 478545; doi: https://doi.org/10.1101/478545

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