Support Options

Submit a Support Ticket

Home Online Presentations [Illinois] MCB 493 Lecture 7: Reinforcement Learning and Associative Conditioning Watch Presentation

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

By Thomas J. Anastasio

University of Illinois at Urbana-Champaign

Published on


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


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.

Cite this work

Researchers should cite this work as follows:

  • Thomas J. Anastasio (2013), "[Illinois] MCB 493 Lecture 7: Reinforcement Learning and Associative Conditioning,"

    BibTex | EndNote



University of Illinois at Urbana-Champaign, Urbana, IL


NanoBio Node, George Michael Daley

University of Illinois at Urbana-Champaign


Oops, We Encountered an Error.

Use the error messages below to try and resolve the issue. If you are still unable to fix the problem report your problem to the system administrator by entering a support ticket.

  1. Unable to find presentation., a resource for nanoscience and nanotechnology, is supported by the National Science Foundation and other funding agencies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.