Introductory Computational Neuroscience

NEURO 120 Subject & Catalog Number

Description

There are 100 billion neurons and over 100 trillion synaptic connections in the human brain. Learning how these neurons interact to recall an old memory, construct sensory perception, and motivate behavior is no trivial venture. Computational techniques have expanded our understanding of the complex systems underlying functions of the brain. In this course, students will be introduced to a variety of tools from fields such as mathematics, physics, and computer science that have been adapted to investigate the principles of neural function. Students will learn concepts and practice skills through lectures, in-class activities, programming assignments, and a final project. Topics covered include biophysical models of neurons, sensory information processing, neural population dynamics, memory, deep learning, reinforcement learning, and techniques to analyze experimental data, among others. Tools will be applied across levels of analysis, from individual neurons to neural population dynamics. Familiarity—but not expertise—with coding and linear algebra will be assumed.