Zoom link: Upon request @[email protected]

Title: Fast and Flexible Group Decision-Making Abstract: A wide range of animals live and move in groups. Many animals do better in groups than alone when, for example, foraging for food, migrating, and avoiding predators. A key to group success is social interaction. Less well understood is how a group, with no centralized control, is…

Monday (9/15) the Continual Learning Group will have a presentation from group member Yunfan Zhang.  Yunfan will be sharing his ongoing work on developing a continual learning benchmark based on deriving up-to-date facts from news over time. Date: Monday, September 15 Time: 3-4pm Room: CSB 480 Zoom: upon request @ [email protected]

Title and Abstract: TBD Zoom: Meeting ID: 993 3345 6502 Passcode: Upon request @ [email protected]

Zoom Link: Upon request @ [email protected]

Title: Neural computations underlying the regulation of motivated behavior Abstract: As we interact with the world around us, we experience a constant stream of sensory inputs, and must generate a constant stream of behavioral actions. What makes brains more than simple input-output machines is their capacity to integrate sensory inputs with an animal’s own internal motivational state…

Next Monday, September 29, we will continue our fall semester program with a group workshop on the topic of neural memory models.  First, we will have a presentation from group member Max Bennett about our ongoing work on generalized neural memory systems that perform flexible updates based on learning instructions specified in natural language.  After the presentation,…

Title and Abstract: TBD

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