Lecturer: Terry Stewart
Fields: Computational Neuroscience
The Neural Engineering Framework provides a general method for programming with neurons. This can be useful both for constructing models of particular biological systems, or for taking advantage of the energy-efficient computation in neuromorphic hardware. In this course, we\’ll introduce the basic ideas of the NEF, but the emphasis will be on hands-on modelling work using the Python software package Nengo. Nengo lets you quickly build and interact with these sorts of neuron models, and was used to construct Spaun, the first (and so far only) large-scale functional brain model capable of performing multiple tasks.
After the initial part of this course where we introduce the tools and methodology, the course will transition to become more project-based, where we can work together to try building models, based on the particular interests of the participants.
- Eliasmith et al., 2012. A large-scale model of the functioning brain. Science, 338:1202-1205. 10.1126/science.1225266
Terry is an Associate Research Officer at National Research Council Canada. Before that, he was a post-doctoral research associate working with Chris Eliasmith at the Centre for Theoretical Neuroscience at the University of Waterloo. His first degree was in engineering, then his masters involved applying experimental psychology on simulated robots, and his Ph.D. was on cognitive modelling. So he self-identifies as a cognitive scientist. He is also a co-founder of Applied Brain Research, a research-based start-up company based around using low-power hardware (neuromorphic computer chips) and adaptive neural algorithms.
Affiliation: National Research Council Canada