Spring 2009 schedule: Monday and Wednesday 3:00-4:30 in 46-5056.
Organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons. Dynamical theories of recurrent networks: amplifiers, attractors, and hybrid computation. Backpropagation and Hebbian learning. Models of perception, motor control, memory, and neural development.
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Prof. Sebastian Seung seung@mit.edu 46-5065 |
TA: Viren Jain viren@mit.edu 46-5065A |
This year we will be using Stellar to provide lecture materials, problem sets, etc. If you are enrolled in the course but unable to access the Stellar site for 9.641, please contact the TA.
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.
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 9.29 Introduction to Computational Neuroscience.