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[Illinois] MCB 493 Lecture 8: Information Transmission and Unsupervised Learning

By Thomas J. Anastasio

University of Illinois at Urbana-Champaign

Published on

Abstract

Unsupervised learning algorithms can train neural networks to increase the amount of information they contain about their inputs and simulate the properties of sensory neurons

8.1 Some Basic Concepts in Information Theory

8.2 Measuring Information Transmission through a Neural Network

8.3 Maximizing Information Transmission in a Neural Network

8.4 Information Transmission and Competitive Learning in Neural Networks

8.5 Information Transmission in Self-Organized Map Networks

8.6 Information Transmission in Stochastic Neural Networks

Submitter

NanoBio Node

University of Illinois at Urbana-Champaign

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 8: Information Transmission and Unsupervised Learning," http://nanohub.org/resources/18833.

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Location

University of Illinois at Urbana-Champaign, Urbana, IL

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