Title: Unifying neural population dynamics, manifold geometry, and circuit structure. Abstract: Single neurons show complex, heterogeneous responses during cognitive tasks, often forming low-dimensional manifolds in the population state space. Consequently, it is…

Title: Mice wiggle a wheel to boost the salience of low visual contrast stimuli Abstract: From the Welsh tidy mouse to the New York City pizza rat, movement belies rodent intelligence. We show that head-fixed…

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…

Title: Training Language Models in Academic: Research Questions and Opportunities Abstract: Large language models have emerged as transformative tools in artificial intelligence, demonstrating unprecedented capabilities in understanding and generating human language. While these models have achieved remarkable performance across a wide range of benchmarks and enabled groundbreaking applications, their development has been predominantly concentrated within…

Title: TBD Abstract: TBD

Title: Task-dependent low-dimensional population dynamics for robustness and learning Abstract: Biological systems face dynamic environments that require flexibly deploying learned skills and continual learning of new tasks. It is not well understood how these systems balance the tension between flexibility for learning and robustness for memory of previous behaviors. Neural activity underlying single, highly controlled…