[Illinois] MCB 493 Lecture 7: Reinforcement Learning and Associative Conditioning

By Thomas J. Anastasio

University of Illinois at Urbana-Champaign

Published on

Abstract

Reinforcement learning algorithms can simulate certain types of associative conditioning and train neural networks to form non-uniform distributed representations.

7.1 Learning the Labeled-Line Task via Perturbation of One Weight at a Time

7.2 Perturbing All Weights Simultaneously and the Importance of Structure

7.3 Plausible Weight Modification using Perturbative Reinforcement Learning

7.4 Reinforcement Learning and Non-Uniform Distributed Representations

7.5 Reinforcement in a Schema Model of Avoidance Conditioning

7.6 Exploration and Exploitation in a Model of Avoidance Conditioning

Bio

THOMAS J. ANASTASIO SYSTEMS NEUROBIOLOGIST I am an associate professor in the University of Illinois Department of Molecular and Integrative Physiology and a full-time faculty member of the Beckman Institute. My main research interest is in Systems Neurobiology, which I define as the application of conceptual and computational methods to complex and multilevel problems in neurobiology. I am also involved in teaching, writing, and technology transfer.

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Researchers should cite this work as follows:

  • Thomas J. Anastasio (2013), "[Illinois] MCB 493 Lecture 7: Reinforcement Learning and Associative Conditioning," http://nanohub.org/resources/18832.

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Location

University of Illinois at Urbana-Champaign, Urbana, IL

Submitter

NanoBio Node, George Michael Daley

University of Illinois at Urbana-Champaign

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