[Illinois] MCB 493 Lecture 5: Unsupervised Learning and Distributed Representations

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Unsupervised learning algorithms, given only a set of input patterns, can train neural networks to form distributed representations of those patterns that resemble brain maps.

5.1 Learning through Competition to Specialize for Specific Inputs

5.2 Training Few Output Neurons to Represent Many Input Patterns

5.3 Simulating the Formation of Brain Maps using Cooperative Mechanisms

5.4 Modeling the Formation of Tonotopic Maps in the Auditory System

5.5 Simulating the Development of Orientation Selectivity in the Visual Cortex

5.6 Modeling a Possible Multisensory Map in the Superior Colliculus


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 5: Unsupervised Learning and Distributed Representations," http://nanohub.org/resources/16951.

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University of Illinois at Urbana-Champaign, Urbana, IL


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