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Baseline control of optimal performance in recurrent neural networks

View ORCID ProfileShun Ogawa, View ORCID ProfileFrancesco Fumarola, View ORCID ProfileLuca Mazzucato
doi: https://doi.org/10.1101/2022.05.11.491436
Shun Ogawa
1Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Hirosawa, Wako, Saitama 351-0198, Japan
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Francesco Fumarola
1Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Hirosawa, Wako, Saitama 351-0198, Japan
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Luca Mazzucato
2Institute of Neuroscience and Departments of Biology, Mathematics and Physics, University of Oregon,Eugene, OR 97403, USA
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  • For correspondence: lmazzuca@uoregon.edu
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Abstract

Changes in behavioral state, such as arousal and movements, strongly affect neural activity in sensory areas. Recent evidence suggests that they may be mediated by top-down projections regulating the statistics of baseline input currents to sensory areas, inducing qualitatively different effects across sensory modalities. What are the computational benefits of these baseline modulations? We investigate this question within a brain-inspired framework for reservoir computing, where we vary the quenched baseline inputs to a random neural network. We found that baseline modulations control the dynamical phase of the reservoir network, unlocking a vast repertoire of network phases. We uncover a new zoo of bistable phases exhibiting the simultaneous coexistence of fixed points and chaos, of two fixed points, and of weak and strong chaos. Crucially, we discovered a host of novel phenomena, including noise-driven enhancement of chaos and ergodicity breaking; neural hysteresis, whereby transitions across phase boundary retain the memory of the initial phase. Strikingly, we found that baseline control can achieve optimal performance without any fine tuning of recurrent couplings. In summary, baseline control of network dynamics opens new directions for brain-inspired artificial intelligence and provides a new interpretation for the ubiquitously observed behavioral modulations of cortical activity.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
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 11, 2022.
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Baseline control of optimal performance in recurrent neural networks
Shun Ogawa, Francesco Fumarola, Luca Mazzucato
bioRxiv 2022.05.11.491436; doi: https://doi.org/10.1101/2022.05.11.491436
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Baseline control of optimal performance in recurrent neural networks
Shun Ogawa, Francesco Fumarola, Luca Mazzucato
bioRxiv 2022.05.11.491436; doi: https://doi.org/10.1101/2022.05.11.491436

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