Title: Putative synaptic theory of temporal order encoding in working memory (Joint work with Gianluigi Mongillo) Abstract: Overwhelming evidence indicates that working memory automatically encodes incoming stimuli in the correct presentation order.…

Speaker: Markus Marks (Caltech) Title: Automating Analysis in Biology Using AI, From Data to Discovery Time and Place: Davis Auditorium, 11:40am, Tuesday April 9 Abstract: Thanks to improved sensors and…

Weekly Meeting Group Discussion: Paper Topic: https://direct.mit.edu/neco/article/35/11/1797/117579/Reducing-Catastrophic-Forgetting-With-Associative Zoom: https://columbiauniversity.zoom.us/j/94783759415?pwd=cTlDTDdCVk9vdEV0QzRKL0hKQW1Kdz09

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.…

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…

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…

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…

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…

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),…

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…