Lecturer: Terrence Stewart
Fields: Computational Neuroscience, Neuroscience, AI
Content
This course provides an overview of computational neuroscience, the science of creating computer simulations of neurons, groups of neurons, and different brain systems, and then comparing the results of these simulations to the behaviour of real brains. This lets us better understand how brains work, and it also has the potential of inspiring new types of Artificial Intelligence systems.
We start by looking at individual neurons and their details, then move to the three major approaches to making large-scale models capable of producing detailed behaviour: Parallel Distributed Processing (PDP++/Emergent), Dynamic Neural Fields (DNF/Cedar), and the Neural Engineering Framework (NEF/Nengo). Python notebooks will be provided for hands-on examples.
Session 1: Individual neurons
Session 2: Many neurons in parallel (PDP++)
Session 3: Dynamic Neural Fields (DNF)
Session 4: The Neural Engineering Framework and Nengo
Literature
- Kriegeskorte, N., & Douglas, P. K. (2018). Cognitive computational neuroscience. Nature neuroscience, 21(9), 1148–1160. https://doi.org/10.1038/s41593-018-0210-5
- Rumelhart, D., & McClelland, J., (1986). Parallel distributed processing: Explorations in the microstructure of cognition. MIT Press, Cambridge, MA, USA
- Schöner, G. (2023). Dynamical Systems Approaches to Cognition. In Sun, Ron (Ed.), The Cambridge Handbook of Computational Cognitive Sciences (2nd ed.). Cambridge University Press.
- Stewart, T.C., & Eliasmith, C. (2014). Large-scale synthesis of functional spiking neural circuits. Proceedings of the IEEE, 102(5):881–898.
Lecturer
Terry Stewart is a Senior Research Officer at the National Research Council Canada, and Site Lead of the NRC-University Waterloo Collaboration Centre. His research includes large-scale brain simulation, cognitive modelling, energy-efficient neuromorphic computing, and AI safety.
Affiliation: National Research Council Canada