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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/24706

Title: Adaptive Optimal-control Algorithms for Brainlike Networks
Authors: Chinta Venkateswararao, Lakshminarayan
Advisor: Tweed, Douglas
Department: Physiology
Issue Date: 5-Aug-2010
Abstract: Many neural control systems are at least roughly optimized, but how is optimal control learned in the brain? There are algorithms for this purpose, but in their present forms they aren’t suited for biological neural networks because they rely on a type of communication that isn’t available in the brain, namely weight transport — transmitting the strengths, or “weights”, of individual synapses to other synapses and neurons. Here I show how optimal control can be learned without weight transport. I explore three complementary approaches. In the first, I show that the control-theory concept of feedback linearization can form the basis for a simple mechanism that learns roughly optimal control, at least in some sensorimotor tasks. Second, I describe a method based on Pontryagin’s Minimum Principle of optimal control, by which a network without weight transport might achieve optimal open-loop control. Third, I describe a mechanism for building optimal feedback controllers, without weight transport, by a method based on generalized Hamilton-Jacobi-Bellman equations. Finally, I argue that the issues raised in these three projects apply quite broadly, i.e. most control algorithms rely on weight transport in many different ways, but it may be possible to recast them into forms that are free of such transport by the mechanisms I propose.
URI: http://hdl.handle.net/1807/24706
Appears in Collections:Doctoral
Department of Physiology - Doctoral theses

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