Synchronization and Redundancy: Implications for Robustness of Neural Learning and Decision Making
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Bouvrie-2011-Synchronization and.pdf
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Author(s)
Slotine, Jean-Jacques E.
Bouvrie, Jacob Vincent
Date Issued
September 2011
Journal
Neural Computation
Publisher
MIT Press
Citation
Bouvrie, Jake, and Jean-Jacques Slotine. “Synchronization and Redundancy: Implications for Robustness of Neural Learning and Decision Making.” Neural Computation 23.11 (2011): 2915-2941. Web. 20 Jan. 2012. © 2011 Massachusetts Institute of Technology
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Final published version
Abstract
Learning and decision making in the brain are key processes critical to survival, and yet are processes implemented by nonideal biological building blocks that can impose significant error. We explore quantitatively how the brain might cope with this inherent source of error by taking advantage of two ubiquitous mechanisms, redundancy and synchronization. In particular we consider a neural process whose goal is to learn a decision function by implementing a nonlinear gradient dynamics. The dynamics, however, are assumed to be corrupted by perturbations modeling the error, which might be incurred due to limitations of the biology, intrinsic neuronal noise, and imperfect measurements. We show that error, and the associated uncertainty surrounding a learned solution, can be controlled in large part by trading off synchronization strength among multiple redundant neural systems against the noise amplitude. The impact of the coupling between such redundant systems is quantified by the spectrum of the network Laplacian, and we discuss the role of network topology in synchronization and in reducing the effect of noise. We discuss range of situations in which the mechanisms we model arise in brain science and draw attention to experimental evidence suggesting that cortical circuits capable of implementing the computations of interest here can be found on several scales. Finally, simulations comparing theoretical bounds to the relevant empirical quantities show that the theoretical estimates we derive can be tight.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Nonlinear Systems Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1162/NECO_a_00183
