This course develops and applies a general framework for understanding neural computation in the context of recent advances in theoretical and experimental neuroscience. Particular emphasis is placed on understanding representation, nonlinear computation, and dynamics in real neurobiological systems. Students will apply signal processing, control theory, linear algebra, probability theory, and similar quantitative tools for the purpose of modelling sensory, motor, and cognitive systems. Topics covered include single neuron function, neural coding, neural dynamics, attractor networks, learning, statistical inference, locomotion, working memory, etc. Familiarity with neural systems is helpful but not essential. [Offered: W] Prereq: SYDE 252
