Title: Building mechanistic models of neural computations with simulation-based machine learning Abstract: Experimental techniques now make it possible to measure the structure and function of neural circuits at an unprecedented scale and resolution. How can we leverage this wealth of data to understand how neural circuits perform computations underlying behaviour? A mechanistic understanding will require models that…

Title: Do contemporary, machine-executable models (aka digital twins) of the primate ventral visual system unlock the ability to non-invasively, beneficially modulate high level brain states? Abstract: In this talk, I will first briefly review the story of how neuroscience, cognitive science and computer science (“AI”) converged to create specific, image-computable, deep neural network models intended…

Zoom Link:  https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1

Seminar Time: 11:30am Date: 11/8/2024 Location: JLG, L5-084  Host: Krishan Kumar   Title: Building mechanistic models of neural computations with simulation-based machine learnin

Title: Wandering Within a World A discussion on Wandering Within a World: Online Contextualized Few-Shot Learning, this 2021 paper by our very own Rich Zemel leverages contextual information in a continually changing environment to improve model performance in realistic settings. Zoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1

Title: TBD Abstract: TBD

Title: Statistical mechanics of transfer learning in the proportional limit Abstract: Transfer learning (TL) is a well-established machine learning technique to boost the generalization performance on a specific (target) task using information gained from a related (source) task, and it crucially depends on the ability of a network to learn useful features. I will present…