Title: Micron brain data at scale: computational challenges in imaging and analysis. Abstract: Uncovering the principles of neural computation requires 1) new methods to observe micron-level targets at scale and 2) interpretable models of high-dimensional time-series. In this talk I will cover recent advances in leveraging advanced data models based on latent sparsity and low-dimensionality to tackle key challenges in both domains. First…

Title: Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning Abstract: The body of an animal determines how the nervous system produces behavior. Therefore, detailed modeling of the neural control of sensorimotor behavior requires a detailed model of the body. Here we contribute an anatomically-detailed biomechanical whole-body model of the fruit fly {\em…

Queens, NY – The New York Hall of Science (NYSCI), the AI Institute for Artificial and Natural Intelligence (ARNI), and the Fu Foundation School of Engineering and Applied Science at Columbia University will feature an engaging panel discussion exploring recent developments in quantum computing and AI. The goal of the discussion is to provide an…

Weekly Meeting Group Discussion: Saket Navlakha, Associate Professor at Cold Spring Harbor Labs (Available via Zoom) Saket Navlakha, Associate Professor at Cold Spring Harbor Labs, will present his work, "Reducing Catastrophic Forgetting With Associative Learning: A Lesson From Fruit Flies". In this work, the authors identified a two-layer neural circuit in the fruit fly olfactory…

Title: The auditory cortex: A sensorimotor fulcrum for speech and music perception Abstract: The Auditory cortex sits at the center of all auditory-motor tasks and percepts, from listening to our voice as we speak, to the music that we play, and to the complex sound mixtures that we seek to perceive. The auditory cortex orchestrates…

Title: Teaching Large Language Models to Reason with Reinforcement Learning Abstract: In this talk, I will discuss how we can use Reinforcement Learning (RL) to improve reasoning in Large Language Models (LLM), as well as when, where, and how to refine LLM reasoning. First, we study how different RL-like algorithms can improve LLM reasoning. We investigate both…

Title: Phase transition in learning with neural networks  Abstract: Statistical physics has studied exactly solvable models of neural networks for more than four decades. In this talk, we will put this line of work in perspective of recent questions stemming from deep learning. We will describe several types of phase transition that appear in the…

Title: Neural basis for skilled movements Abstract: Generating behavior is an incredible achievement of the nervous system, considering the range of possible actions and the complexity of musculoskeletal arrangements. Motor control involves understanding the surrounding environment, selecting appropriate plans, converting those plans into motor commands, and adaptively reacting to feedback. This seminar will review efforts…

Title: Scope of the working group, example project, and literature Short Description: From Nikolaus Kriegeskorte's (Professor of Psychology and of Neuroscience (in the Mortimer B. Zuckerman Mind Brain Behavior Institute) lab, Eivinas Butkus (grad student) will show an example of a modeling project optimizing energetic demands along with accuracy in a vision task, and Josh…

Title: Efficient coding in reward neurons Abstract: Two of the greatest triumphs of computational neuroscience have been efficient coding accounts of tuning properties of sensory neurons and reinforcement learning accounts of dopaminergic neurons in the midbrain. At first glance, these theories seem to have no connection, but I will argue that they do. One can…