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X-WR-CALDESC:Events for ARNI
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251003T113000
DTEND;TZID=America/New_York:20251003T130000
DTSTAMP:20250923T155204Z
CREATED:20250923T155204Z
LAST-MODIFIED:20250923T155204Z
UID:1997-1759491000-1759496400@arni-institute.org
SUMMARY:CTN: Reza Shadmehr
DESCRIPTION:Title and Abstract: TBD
URL:https://arni-institute.org/event/ctn-reza-shadmehr/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251003T140000
DTEND;TZID=America/New_York:20251003T150000
DTSTAMP:20250922T183104Z
CREATED:20250922T183104Z
LAST-MODIFIED:20250922T183104Z
UID:1994-1759500000-1759503600@arni-institute.org
SUMMARY:ARNI Biological Learning Working Group
DESCRIPTION:Biological Learning first fall 2025 working group session of the year! Pingsheng Li\, PhD student with Blake Richards at MILA\, will be presenting Log-Normal Multiplicative Dynamics for Stable Low-Precision Training of Large Networks.\n \nBrief discussion of how the group can all collaborate together on a project\, define a benchmark for ourselves with some metrics we care about\, and then the group will break into pods that each will develop methods towards solving the benchmark tasks. \nGoogle Meets: upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/arni-biological-learning-working-group-3/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251008T140000
DTEND;TZID=America/New_York:20251008T150000
DTSTAMP:20251006T170343Z
CREATED:20250917T150021Z
LAST-MODIFIED:20251006T170343Z
UID:1991-1759932000-1759935600@arni-institute.org
SUMMARY:ARNI Frontier Models for Neuroscience and Behavior Working Group
DESCRIPTION:Title:\nOmniMouse: Scaling properties of multi-modal\, multi-task Brain Models on 150B Neural Tokens \nAbstract:\nScaling data and artificial neural networks has transformed AI\, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains unclear. Here we leveraged a dataset of 3.3 million neurons from the visual cortex of 78 mice across 323 sessions\, totaling more than 150 billion neural tokens recorded during natural movies\, images and parametric stimuli\, and behavior. We train multi-modal\, multi-task transformer models (1M–300M parameters) that support three regimes flexibly at test time: neural prediction (predicting neuronal responses from sensory input and behavior)\, behavioral decoding (predicting behavior from neural activity)\, neural forecasting (predicting future activity from current neural dynamics)\, or any combination of the three. We find that performance scales reliably with more data\, but gains from increasing model size saturate — suggesting that current brain models are limited by data rather than compute. This inverts the standard AI scaling story: in language and computer vision\, massive datasets make parameter scaling the primary driver of progress\, whereas in brain modeling — even in the mouse visual cortex\, a relatively simple and low-resolution system — models remain data-limited despite vast recordings. These findings highlight the need for richer stimuli\, tasks\, and larger-scale recordings to build brain foundation models. The observation of systematic scaling raises the possibility of phase transitions in neural modeling\, where larger and richer datasets might unlock qualitatively new capabilities\, paralleling the emergent properties seen in large language models. \nZoom: Upon request @ arni@columbia.edu \n 
URL:https://arni-institute.org/event/arni-frontier-models-for-neuroscience-and-behavior-working-group/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251010T113000
DTEND;TZID=America/New_York:20251010T130000
DTSTAMP:20251007T171403Z
CREATED:20250923T155316Z
LAST-MODIFIED:20251007T171403Z
UID:1999-1760095800-1760101200@arni-institute.org
SUMMARY:CTN: Maryam Shanechi
DESCRIPTION:Title: Dynamical models of neural-behavioral data with application to AI-driven neurotechnology \nAbstract: A major challenge in neuroAI is to model\, decode\, and modulate the activity of large populations of neurons that underlie our brain’s functions and dysfunctions. Toward addressing this challenge\, I will present our work on novel dynamical models of neural-behavioral data and applying them to enable a new generation of brain-computer interfaces for disorders such as major depression. First\, I will present a novel dynamical modeling framework that jointly describes neural-behavioral data\, dissociates behaviorally relevant neural dynamics\, and learns them more accurately. Then\, I will show how we can also predict the effect of inputs\, such as sensory stimuli or neurostimulation\, to dissociate intrinsic and input-driven neural dynamics. I further present how these models can incorporate multiple spatiotemporal scales of brain activity simultaneously\, from spikes to LFP to brain-wide neuroimaging. Finally\, I will discuss the challenge of developing AI algorithms for neurotechnology. I will present a framework that combines neural networks with stochastic state-space models to enable accurate yet flexible inference of brain states causally\, non-causally\, and even with missing neural samples. The above dynamical models can enable next-generation AI-driven neurotechnologies that restore lost motor and emotional function in diverse brain disorders such as paralysis and major depression.
URL:https://arni-institute.org/event/ctn-maryam-shanechi/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251017T113000
DTEND;TZID=America/New_York:20251017T130000
DTSTAMP:20250923T155517Z
CREATED:20250923T155504Z
LAST-MODIFIED:20250923T155517Z
UID:2001-1760700600-1760706000@arni-institute.org
SUMMARY:CTN: Ilana Witten
DESCRIPTION:Title and Abstract: TBD
URL:https://arni-institute.org/event/ctn-illana-witten/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251021T130000
DTEND;TZID=America/New_York:20251021T150000
DTSTAMP:20251008T221401Z
CREATED:20251008T213444Z
LAST-MODIFIED:20251008T221401Z
UID:2007-1761051600-1761058800@arni-institute.org
SUMMARY:Speaker: Jascha Achterberg ARNI WG Multi-resource-cost optimization of neural network models
DESCRIPTION:Title:\nBuilding the brain’s efficient system-level architecture: optimisations across space\, time\, and multiple regions \nAbstract:\nThe computations a brain can perform are fundamentally constrained by physical realities: energetic resources are limited\, and time is precious. To understand why the brain works the way it does\, we must understand its function in the context of these constraints. Prior modeling work has successfully demonstrated how spatial energetic constraints drive structure-function co-optimization\, giving rise to many of the architectural features we observe across areas of neuroscience. By incorporating such physical constraints\, we can build complex systems-level models that are meaningfully constrained by physically measurable factors rather than arbitrary design choices. \nIn this talk\, I will expand on these spatial frameworks by introducing new work on temporal processing and signal precision constraints in neural networks. I will demonstrate how different optimization strategies within individual regions can be combined in heterogeneous multi-region models\, revealing how the brain trades off resource use across tasks and situations. Finally\, I will show how space and time interact in surprising ways to achieve efficient computation — principles that apply not only to the brain but to any large-scale distributed computing system. Together\, these advances bring us closer to understanding the general principles that enable sophisticated intelligence to emerge from physically and energetically constrained computing systems.
URL:https://arni-institute.org/event/speaker-jascha-achterberg-arni-wg-multi-resource-cost-optimization-of-neural-network-models/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251024T113000
DTEND;TZID=America/New_York:20251024T130000
DTSTAMP:20251021T182719Z
CREATED:20250917T182936Z
LAST-MODIFIED:20251021T182719Z
UID:1992-1761305400-1761310800@arni-institute.org
SUMMARY:CTN: Anna Schapiro
DESCRIPTION:Title: Learning representations of specifics and generalities over time\n\nAbstract: There is a fundamental tension between storing discrete traces of individual experiences\, which allows recall of particular moments in our past without interference\, and extracting regularities across these experiences\, which supports generalization and prediction in similar situations in the future. One influential proposal for how the brain resolves this tension is that it separates the processes anatomically into Complementary Learning Systems\, with the hippocampus rapidly encoding individual episodes and the neocortex slowly extracting regularities over days\, months\, and years. But this does not explain our ability to learn and generalize from new regularities in our environment quickly\, often within minutes. We have put forward a neural network model of the hippocampus that suggests that the hippocampus itself may contain complementary learning systems\, with one pathway specializing in the rapid learning of regularities and a separate pathway handling the region’s classic episodic memory functions. This proposal has broad implications for how we rapidly learn novel information of specific and generalized types\, which we test across statistical learning\, inference\, and category learning paradigms. We also explore how this system interacts with slower-learning neocortical memory systems\, with empirical and modeling investigations into how hippocampal replay shapes neocortical representations during sleep. Together\, the work helps us understand how structured information in our environment is initially encoded and how it then transforms over time.\nZoom: Available upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/ctn-anna-schapiro/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251024T140000
DTEND;TZID=America/New_York:20251024T150000
DTSTAMP:20251022T132648Z
CREATED:20251022T132648Z
LAST-MODIFIED:20251022T132648Z
UID:2016-1761314400-1761318000@arni-institute.org
SUMMARY:ARNI Biological Learning Working Group
DESCRIPTION:Continuation from prior meetings about benchmarks and competition proposals. \nZoom Link: upon request @ ARNI@columbia.edu
URL:https://arni-institute.org/event/arni-biological-learning-working-group-4/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251027T150000
DTEND;TZID=America/New_York:20251027T160000
DTSTAMP:20251022T210922Z
CREATED:20251022T210922Z
LAST-MODIFIED:20251022T210922Z
UID:2018-1761577200-1761580800@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group
DESCRIPTION:Next Meeting Info\n\n\nDate: Monday\, October 27\nTime: 3-4pm\nRoom: CEPSR 620\n\nZoom: Upon request @arni@columbia.edu
URL:https://arni-institute.org/event/arni-continual-learning-working-group-2/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251029T150000
DTEND;TZID=America/New_York:20251029T163000
DTSTAMP:20251015T155911Z
CREATED:20251008T214532Z
LAST-MODIFIED:20251015T155911Z
UID:2010-1761750000-1761755400@arni-institute.org
SUMMARY:ARNI Distinguished Seminar Series: Leila Wehbe
DESCRIPTION:Bio: Leila Wehbe is an associate professor in the Machine Learning Department and the Neuroscience Institute at Carnegie Mellon University. Her work is at the interface of cognitive neuroscience and computer science. It combines naturalistic functional imaging with machine learning both to improve our understanding of the brain and to find insight to build better artificial systems. She is the recipient of an NSF CAREER award\, a Google faculty research award and an NIH CRCNS R01. Previously\, she was a postdoctoral researcher at UC Berkeley and obtained her PhD from Carnegie Mellon University \nTitle: Model prediction error reveals separate mechanisms for integrating multi-modal information in the human cortex \nAbstract: Language comprehension engages much of the human cortex\, extending beyond the canonical language system. Yet in everyday life\, language unfolds alongside other modalities\, such as vision\, that recruit these same distributed areas. Because language is often studied in isolation\, we still know little about how the brain coordinates and integrates multimodal representations. In this talk\, we use fMRI data from participants viewing 37 hours of TV series and movies to model the interaction of auditory and visual input. Using encoding models that predict brain activity from each stream\, we introduce a framework based on prediction error that reveals how individual brain regions combine multimodal information.
URL:https://arni-institute.org/event/arni-distinguished-seminar-series-leila-wehbe/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
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