Professors Thomas Palmeri and Sean Polyn
Computational neuroscience is an approach to understanding the information content of neural signals by modeling the nervous system at many different structural scales, including the biophysical, the circuit, and the systems levels. Computer simulations of neurons and neural networks are complementary to traditional techniques of neuroscience.
– T. Sejnowski and T. Poggio
Computational neuroscience is the study of brain function in terms of the information processing properties of the structures that make up the nervous system. It is an interdisciplinary science that links the diverse fields of neuroscience, cognitive science, and psychology with electrical engineering, computer science, mathematics, and physics.
In this course, we will use computational techniques to understand how the brain works. We will discuss developing computer simulation models of networks of neurons in the brain. While we will discuss various ways that neurons are computationally instantiated in these models, detailed biophysical and biochemical models of neurons and their cellular and molecular mechanisms will not be covered. Instead, we will discuss different kinds of neural network architectures, different ways of simulating neural dynamics, and different assumptions about how neural networks learn. These models will be used to simulate fundamental aspects of human cognition, including perception, memory, and decision making. We will also discuss using computational approaches to analyze and understand brain data from fMRI, electrophysiology, or neurophysiology. This will include applying psychological, economic, statistical, or machine learning models to brain data. When appropriate, we will discuss how the models and approaches used in computational neuroscience are applied to domains outside of neuroscience. We will rely heavily on demonstrations and hands on experience – in the form of homework assignments – developing, testing, and evaluating computational models and computational approaches.