9.54 Computational Aspects of Biological Learning


Class Info

Takes a computational approach to learning in the brain by neurons and synapses. Examines supervised and unsupervised learning as well as possible biological substrates, including Hebb synapses and the related topics of Oja flow and principal components analysis. Discusses hypothetical computational primitives in the nervous system, and the implications for unsupervised learning algorithms underlying the development of tuning properties of cortical neurons. Also focuses on a broad class of biologically plausible learning strategies.

This class has 9.40 as a prerequisite.

9.54 will be offered this semester (Fall 2017). It is instructed by .

This class counts for a total of 12 credits.

You can find more information at the 9.54, Fall 2014 site.

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