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Course Details

Spring 2016-2017
* NEU 437 / MOL 437 / PSY 437 (STL)   Graded A-F, P/D/F, Audit

Computational Neuroscience

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 537

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.

Oral Presentation(s) - 50%
Term Paper(s) - 25%
Problem set(s) - 25%

Prerequisites and Restrictions:
Basic linear algebra, probability, ordinary differential equations, and some programming experience, or permission of the instructor..


Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
41164 L01 01:30:00 pm - 02:50:00 pm T Th   Princeton Neuroscience Institu   A02   Enrolled:19 Limit:28
41165 B01 07:30:00 pm - 10:20:00 pm W   Princeton Neuroscience Institu   A02   Enrolled:19 Limit:28