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[Illinois] MCB 493 Lecture 6: Supervised Learning and Non-Uniform Representations

By Thomas J. Anastasio

Department of Molecular and Integrative Physiology , University of Illinois at Urbana-Champaign, Urbana, IL

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Abstract

Supervised learning algorithms can train neural networks to associate patterns and simulate the non-uniform distributed representations found in many brain regions.

6.1 Using the Classic Hebb Rule to Learn a Simple Labeled Line Response

6.2 Learning a Simple Contingency Using the Covariation Rule

6.3 Using the Delta Rule to Learn a Complex Contingency

6.4 Learning Interneuronal Representations using Back-Propagation

6.5 Simulating Catastrophic Retroactive Interference in Learning

6.6 Simulating the Development of Non-Uniform Distributed Representations

6.7 Modeling Non-Uniform Distributed Representations in the Vestibular Nuclei

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 6: Supervised Learning and Non-Uniform Representations," http://nanohub.org/resources/17022.

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Location

University of Illinois at Urbana-Champaign, Urbana, IL

Submitter

NanoBio Node, Obaid Sarvana, George Daley

University of Illinois at Urbana-Champaign

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