Biological Learning first fall 2025 working group session of the year! Pingsheng Li, PhD student with Blake Richards at MILA, will be presenting Log-Normal Multiplicative Dynamics for Stable Low-Precision Training of Large Networks.   Brief discussion of how the group can all collaborate together on a project, define a benchmark for ourselves with some metrics…

Title: OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens Abstract: Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains unclear. Here we leveraged a dataset of 3.3 million neurons from the visual cortex of 78 mice…

Title: Dynamical models of neural-behavioral data with application to AI-driven neurotechnology Abstract: A major challenge in neuroAI is to model, decode, and modulate the activity of large populations of neurons that underlie our brain’s functions and dysfunctions. Toward addressing this challenge, I will present our work on novel dynamical models of neural-behavioral data and applying…

Title and Abstract: TBD

Title: Building the brain’s efficient system-level architecture: optimisations across space, time, and multiple regions Abstract: The computations a brain can perform are fundamentally constrained by physical realities: energetic resources are limited, and time is precious. To understand why the brain works the way it does, we must understand its function in the context of these…

Title: Learning representations of specifics and generalities over time Abstract: There is a fundamental tension between storing discrete traces of individual experiences, which allows recall of particular moments in our past without interference, and extracting regularities across these experiences, which supports generalization and prediction in similar situations in the future. One influential proposal for how the…

Continuation from prior meetings about benchmarks and competition proposals. Zoom Link: upon request @ [email protected]

Next Meeting Info Date: Monday, October 27 Time: 3-4pm Room: CEPSR 620 Zoom: Upon request @[email protected]

Bio: Leila Wehbe is an associate professor in the Machine Learning Department and the Neuroscience Institute at Carnegie Mellon University. Her work is at the interface of cognitive neuroscience and computer science. It combines naturalistic functional imaging with machine learning both to improve our understanding of the brain and to find insight to build better artificial systems. She is…

Seminar Time: 11:30am Date: Fri 11/7/25 Seminar Location: JLG, L5-084 Host: Weijia Zhang Title: Latent causes, prediction errors, and the organization of memory Abstract: No two events are alike. But still, we learn, which means that we implicitly decide what events are similar enough that experience with one can inform us about what to do in another. We have suggested…

Bio Bryan Li is completing his PhD in NeuroAI at the University of Edinburgh, under the supervision of Arno Onken and Nathalie Rochefort. His main PhD project focuses on building deep learning-based encoding models of the visual cortex that accurately predict neural activity in response to arbitrary visual stimuli. Recently, he joined Dario Farina’s lab at…

Continuation from prior meetings about benchmarks and competition proposals. Zoom Link: upon request @ [email protected]