ARNI coPI Kathy McKeown and ARNI faculty Carl Vondrick participate in the Panel 1: Empirical and Technological Questions: Current Landscape, Challenges, and Opportunities Link: https://www.engineering.columbia.edu/symposium-generative-ai-free-speech-public-discourse Article: https://www.engineering.columbia.edu/news/navigating-generative-ai-and-its-impact-future-public-discourse?utm_source=newsletter&utm_medium=email&utm_campaign=highlights030124

Weekly Meeting Group Discussion: Paper Topic: https://arxiv.org/abs/2302.00487 Zoom: https://columbiauniversity.zoom.us/j/3658091817?pwd=WHFJVzAwbDdQcFMzc2FreVplKzVMUT09

Weekly Meeting Group Discussion: Paper Topic: https://arxiv.org/abs/2302.00487 Zoom: https://columbiauniversity.zoom.us/j/97515072030?pwd=VGJONXR6bW9LVTN3VlZZSXdRZnNIdz09

Weekly Meeting Group Discussion: Paper Topic: https://arxiv.org/abs/1906.01076 Zoom: https://columbiauniversity.zoom.us/j/94783759415?pwd=cTlDTDdCVk9vdEV0QzRKL0hKQW1Kdz09

Title: Brain-wide credit assignment: cortical and subcortical perspectives Abstract: The brain assigns credit to trillions of synapses remarkably well. How the brain achieves this feat is one of the great mysteries in neuroscience. Recently, we have introduced Bursting cortico-cortical networks, a computational model of hierarchical credit assignment that captures a large number of biological features while approximating…

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

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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,…