Introduction to neural networks
Master
In Maynard (USA)
Description
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Type
Master
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Location
Maynard (USA)
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Start date
Different dates available
This course explores the organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation are covered. Additional topics include backpropagation and Hebbian learning, as well as models of perception, motor control, memory, and neural development.
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Subjects
- Networks
Course programme
Lectures: 2 sessions / week, 1.5 hours / sessions
The subject will focus on basic mathematical concepts for understanding nonlinearity and feedback in neural networks, with examples drawn from both neurobiology and computer science. Most of the subject is devoted to recurrent networks, because recurrent feedback loops dominate the synaptic connectivity of the brain. There will be some discussion of statistical pattern recognition, but less than in the past, because this perspective is now covered in Machine Learning and Neural Networks. Instead the connections to dynamical systems theory will be emphasized.
Modern research in theoretical neuroscience can be divided into three categories: cellular biophysics, network dynamics, and statistical analysis of neurobiological data. This subject is about the dynamics of networks, but excludes the biophysics of single neurons, which will be taught in 9.29J, Introduction to Computational Neuroscience.
The following text is recommended:
Hertz, John, Anders Krogh, and Richard G. Palmer. Introduction to the Theory of Neural Computation. Redwood City, CA: Addison-Wesley Pub. Co., 1991. ISBN: 9780201515602.
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Introduction to neural networks