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Control strategies for neural populations with rectified activation function
T. Menara, J. Cortés
Proceedings of the IEEE Conference on Decision and Control, Cancun, Mexico, 2022, pp. 649-654

Abstract

In the human brain, highly recurrent cortical circuitry supports large-scale information processing, coordinates learning episodes, and governs the emergence of healthy and diseased states. A key outstanding challenge of neural engineering is to ultimately control the collective dynamics of distinct neural populations, so as to promote the emergence or recovery of desired activity patterns. In this paper, we investigate the control of a general rate model of neural activity with rectified activation function. We first show that any target state in the open positive orthant can be reached in finite time. Furthermore, we present an array of results to perform (feedback and feedforward) efficient control in prototypical classes of networks with distinct connection types and in the case of sparse control inputs. Due to the relevance of rate models in both biological and artificial neural networks, our results lay the groundwork for the development of engineered interventions to enhance the dynamical behavior of in vivo and synthetic neural circuitry.

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Mechanical and Aerospace Engineering, University of California, San Diego
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