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Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks

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Abstract

A large body of experimental and theoretical work on neural coding suggests that the information stored in brain circuits is represented by time-varying patterns of neural activity. Reservoir computing, where the activity of a recurrently connected pool of neurons is read by one or more units that provide an output response, successfully exploits this type of neural activity. However, the question of system robustness to small structural perturbations, such as failing neurons and synapses, has been largely overlooked. This contrasts with well-studied dynamical perturbations that lead to divergent network activity in the presence of chaos, as is the case for many reservoir networks. Here, we distinguish between two types of structural network perturbations, namely local (e.g., individual synaptic or neuronal failure) and global (e.g., network-wide fluctuations). Surprisingly, we show that while global perturbations have a limited impact on the ability of reservoir models to perform various tasks, local perturbations can produce drastic effects. To address this limitation, we introduce a new architecture where the reservoir is driven by a layer of oscillators that generate stable and repeatable trajectories. This model outperforms previous implementations while being resistant to relatively large local and global perturbations. This finding has implications for the design of reservoir models that capture the capacity of brain circuits to perform cognitively and behaviorally relevant tasks while remaining robust to various forms of perturbations. Further, our work proposes a novel role for neuronal oscillations found in cortical circuits, where they may serve as a collection of inputs from which a network can robustly generate complex dynamics and implement rich computations.

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Acknowledgments

This work was supported by a Discovery grant from the Natural Sciences and Engineering Council of Canada (NSERC Grant No. 210977 and No. 210989), operating funds from the Canadian Institutes of Health Research (CIHR Grant No. 6105509), and the University of Ottawa Brain and Mind Institute (uOBMI), scholarships awarded to PVL from the Ontario Graduate Scholarship (OGS) and the Fonds de recherche Nature et technologies (FQRNT) as well as a FQRNT postdoctoral fellowship, a Bernstein Fellowship and an Innovation Fellowship from the Washington Research Foundation to GL. We would like to thank Eric S. Kuebler and Nareg Berberian for their helpful comments.

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Authors and Affiliations

  1. School of Psychology and Center for Neural Dynamics, University of Ottawa, Ottawa, Ontario, Canada

    Philippe Vincent-Lamarre & Jean-Philippe Thivierge

  2. UW Institute for Neuroengineering, University of Washington, Seattle, Washington, US

    Guillaume Lajoie

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  1. Philippe Vincent-Lamarre
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Correspondence to Philippe Vincent-Lamarre.

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Vincent-Lamarre, P., Lajoie, G. & Thivierge, JP. Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks. J Comput Neurosci 41, 305–322 (2016). https://doi.org/10.1007/s10827-016-0619-3

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