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
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.
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.
University of Illinois at Urbana-Champaign, Urbana, IL