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Myopic control of neural dynamics.

David Hocker1, Il Memming Park1,2,3

  • 1Department of Neurobiology and Behavior Stony Brook University, Stony Brook, New York, United States of America.

Plos Computational Biology
|March 12, 2019
PubMed
Summary

We developed a novel "myopic" controller for neural systems that adapts to real-time dynamics, unlike rigid traditional methods. This approach effectively manipulates neural activity for cognitive and clinical applications.

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Area of Science:

  • Neuroscience
  • Control Theory
  • Computational Neuroscience

Background:

  • Current control schemes for neural systems do not adequately address their inherent variability and real-time dynamics.
  • Effective manipulation requires controllers that adapt to the current neural state, independent of past trajectories.

Purpose of the Study:

  • To propose a novel "myopic" controller within a nonlinear state-space feedback framework for manipulating neural dynamics.
  • To demonstrate the controller's ability to transform one dynamical system into another, overcoming limitations of existing methods.

Main Methods:

  • Formulation of a "myopic" controller using a novel variant of a model reference control cost.
  • Implementation of a short-sighted control strategy targeting a single time step into the future.
  • Application of the controller to two distinct neuroscience examples: cognitive processes and neurological disorders.

Main Results:

  • The myopic controller successfully transformed a winner-take-all decision-making system into a neural integrator, probing the dynamics-behavior link.
  • An unhealthy motor-like system with a beta-oscillation attractor was effectively controlled to function as a healthy motor system.

Conclusions:

  • The proposed myopic controller offers a flexible and computationally efficient alternative for manipulating neural dynamics.
  • This approach has broad utility in neuroscience research for understanding cognition and in clinical settings for treating neurological disorders.