Weekly Meeting Group Discussion: Paper Topic: https://arxiv.org/abs/2102.01951 Zoom: https://columbiauniversity.zoom.us/j/94783759415?pwd=cTlDTDdCVk9vdEV0QzRKL0hKQW1Kdz09

Title: Multiplexing multiple signals in neural codes: new statistical tools and evidence Abstract: How the brain represents multiple objects is mysterious. Sensory neurons are broadly tuned, producing overlap in the populations of neurons potentially activated by each object in the scene. This overlap raises questions about how distinct information is retained about each item. I…

Title: Brain Decodes Deep Nets Presenter: Jianbo Shi, PhD GRASP Laboratory Computer and Information Science University of Pennsylvania Abstract: We developed a surprising usage of brain encoding: using a brain fMRI prediction model to draw a picture of how a deep net processes information onto a brain.  Our tool provides a detailed analysis of large pre-trained vision models…

Weekly Meeting Group Discussion: Paper Topic: https://arxiv.org/abs/2302.03241 Zoom: https://columbiauniversity.zoom.us/j/94783759415?pwd=cTlDTDdCVk9vdEV0QzRKL0hKQW1Kdz09

"The Data Science Institute’s flagship annual event connects innovators in industry and government to Columbia researchers who are propelling advances across every sector with data science." If you are interested in the event please register on their event page.

Weekly Meeting Group Discussion: Paper Topic: https://arxiv.org/abs/2309.10105 Zoom: https://columbiauniversity.zoom.us/j/94783759415?pwd=cTlDTDdCVk9vdEV0QzRKL0hKQW1Kdz09

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. How this is achieved in the brain is however not well understood. We addressed this issue in the framework of our previously proposed synaptic theory,…

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 decreasing data acquisition and storage costs, biologists are increasingly able to collect more and higher quality data. How can we harness the expanding capabilities of…

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. 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…