[Illinois] MCB 493 Lecture 9: Probability Estimation and Supervised Learning

By Thomas J. Anastasio

University of Illinois at Urbana-Champaign

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Abstract

Supervised learning algorithms can train neural units and networks to estimate probabilities and simulate the responses of neurons to multisensory stimulation

9.1 Implementing a Simple Classifier as a Three-Layered Neural Network

9.2 Predicting Rain as an Everyday Example of Probabilistic Inference

9.3 Implementing a Simple Classifier Using Bayes' Rule

9.4 Modeling Neural Responses to Sensory Input as a Probabilistic Inference

9.5 Modeling Multisensory Collicular Neurons as Probability Estimators

9.9.Simulating the Development of Non-Uniform Distributed Representations

9.7 Modeling Non-Uniform Distributed Representations in the Vestibular Nuclei

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Researchers should cite this work as follows:

  • Thomas J. Anastasio (2013), "[Illinois] MCB 493 Lecture 9: Probability Estimation and Supervised Learning," http://nanohub.org/resources/18834.

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University of Illinois at Urbana-Champaign, Urbana, IL

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University of Illinois at Urbana-Champaign

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