Courses
COURSE ANNOUNCEMENT:
Computational Neuroscience
HARVARD GSAS: Neuro/MCB/Physics 231 (cross-listed in SEAS)
Course number: 49249
Mondays and Wednesdays 03:00 PM – 04:15 PM
Description: This course explores Contemporary Brain Theory spanning local neuronal circuits as well as deep neural networks; examines the relationship between network structure, dynamics, and computation; introduces analytical and numerical tools from information theory, dynamical systems, statistics, statistical physics, AI, and machine learning for the study of neural computation. Key topics include computational principles in early sensory systems; methods in unsupervised and supervised learning; attractors, memory, and spatial functions in cortical circuits; understanding noise, chaos, and neural coding; and exploring learning, representations, and cognitive functions in deep neural networks in brains and machines.
Prerequisites: Basic knowledge of multivariate calculus, differential equations, linear algebra, multivariate probability theory, and scientific programming. This course is aimed at graduate students and advanced undergraduates.
Questions? Email:
Nathan Sun: nsun@college.harvard.edu
Tanishq Kumar: tkumar@college.harvard.edu
Alex Van Meegen: avanmeegen@fas.harvard.edu
Haim Sompolinsky: hsompolinsky@mcb.harvard.edu
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PAST:
PHYSICS 265 2023 Spring / Full Term / Section: 001 / Class number: 18434
Monday 03:00 PM – 04:15 PM; Wednesday 03:00 PM – 04:15 PM
Statistical Mechanics of Spin Glasses and Neural Networks
The course will survey advanced statistical physics approaches in the study of complex natural and artificial systems, spanning theory of spin glasses, random matrices, random dynamical systems, random graphs, and neural networks, with applications to the physics of spin glasses, chaos in random circuits, memory and learning in recurrent and deep neural networks. Surveyed methods include Replica Theory, Dynamic Mean Fields, Cavity and Message Passing, Kernels and Gaussian Processes.