This course features a selection of downloadable lecture notes and problem sets in the assignments section.
» View an older version of this course en Español or em Portugues courtesy of Universia. Please note that since our last publication, the translated version available may not have the most current content that is available on the MIT OCW site.
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.
Technical Requirements
Special software is required to use some of the files in this course: .mat, and .m.