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Course Details

Spring 2014-2015
NEU 537 / MOL 537 / PSY 517   Graded A-F, P/D/F, Audit

Computational Neuroscience and Computing Networks

Carlos D. Brody

Introduction to a mathematical description of how networks of neurons can represent information and compute with it. Course will survey computational modeling and data analysis methods for neuroscience. Topics will include representation of visual information, spatial navigation, short-term memory, and decision-making. Two 90 minute lectures, one laboratory. Lectures in common with NEU 437. Graduate students will carry out and write up an in-depth semester-long project.

Sample reading list:
Gerstner et al.,, Neuronal Dynamics
Dayan, P. and Abbott, L., Theoretical Neuroscience
Ballard, D.H., Introduction to Natural Computation
Kandel, E., Schwartz, J. and Jessell, T., Principles of Neural Science

Reading/Writing assignments:
Problem sets, oral presentations, and a project paper.


Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
41272 L01 03:00:00 pm - 04:20:00 pm T Th   Princeton Neuroscience Institu   A30   Enrolled:3 Limit:7
41273 B01 07:30:00 pm - 10:20:00 pm W   Princeton Neuroscience Institu   A30   Enrolled:3 Limit:7