Guest: Roberto Budzinski from the University of Lethbridge
Topic: A mathematical framework to link structure, dynamics, and computation in oscillator networks.
Date: Monday, March 23
Location: Markin Hall M1060
Time: 12:15 - 1:15 p.m.
Understanding how network structure gives rise to spatiotemporal dynamics and computation is a central challenge in computational neuroscience and artificial intelligence. Despite increasingly detailed connectomic data in neuroscience and large-scale datasets in machine learning, establishing principled links between connectivity, dynamics, and function in nonlinear neural systems remains difficult. In this talk, I will present a mathematical framework that directly relates network architecture to emergent dynamical patterns and computational capabilities in analytically tractable models. Our approach focuses on networks of coupled oscillators, which are widely used to model interacting neural populations and have recently gained interest as computational substrates in artificial neural networks. With this approach, we can show how key structural features of these networks — including connectivity patterns and transmission delays — determine the emergence and stability of spatiotemporal activity, enabling analytical predictions of collective phenomena such as traveling waves.
Contact:
Cherie Secrist | cherie.secrist@uleth.ca | (403) 329-2470