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X-WR-CALDESC:Events for ARNI
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260202T150000
DTEND;TZID=America/New_York:20260202T160000
DTSTAMP:20260513T201737
CREATED:20260130T145319Z
LAST-MODIFIED:20260130T145319Z
UID:2310-1770044400-1770048000@arni-institute.org
SUMMARY:Speaker: Thuy Nguyen – ARNI Frontier Models for Neuroscience and Behavior Working Group
DESCRIPTION:Title:A multimodal sleep foundation model for disease prediction \nAbstract: Sleep is a fundamental biological process with broad implications for physical and mental health\, yet its complex relationship with disease remains poorly understood. Polysomnography (PSG)—the gold standard for sleep analysis—captures rich physiological signals but is underutilized due to challenges in standardization\, generalizability and multimodal integration. To address these challenges\, we developed SleepFM\, a multimodal sleep foundation model trained with a new contrastive learning approach that accommodates multiple PSG configurations. Trained on a curated dataset of over 585\,000 hours of PSG recordings from approximately 65\,000 participants across several cohorts\, SleepFM produces latent sleep representations that capture the physiological and temporal structure of sleep and enable accurate prediction of future disease risk. From one night of sleep\, SleepFM accurately predicts 130 conditions with a C-Index of at least 0.75 (Bonferroni-corrected P < 0.01)\, including all-cause mortality (C-Index\, 0.84)\, dementia (0.85)\, myocardial infarction (0.81)\, heart failure (0.80)\, chronic kidney disease (0.79)\, stroke (0.78) and atrial fibrillation (0.78). Moreover\, the model demonstrates strong transfer learning performance on a dataset from the Sleep Heart Health Study—a dataset that was excluded from pretraining—and performs competitively with specialized sleep-staging models such as U-Sleep and YASA on common sleep analysis tasks\, achieving mean F1 scores of 0.70–0.78 for sleep staging and accuracies of 0.69 and 0.87 for classifying sleep apnea severity and presence. This work shows that foundation models can learn the language of sleep from multimodal sleep recordings\, enabling scalable\, label-efficient analysis and disease prediction.
URL:https://arni-institute.org/event/speaker-thuy-nguyen-arni-frontier-models-for-neuroscience-and-behavior-working-group/
LOCATION:MILA\, A14
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260206T113000
DTEND;TZID=America/New_York:20260206T130000
DTSTAMP:20260513T201737
CREATED:20260204T163719Z
LAST-MODIFIED:20260204T163719Z
UID:2336-1770377400-1770382800@arni-institute.org
SUMMARY:CTN: Herbert Zheng Wu
DESCRIPTION:Herbert Zheng Wu \nTitle: Neural Basis of Leader–Follower Dynamics in Cooperative Behavior \n\nAbstract: Cooperation allows social species to achieve outcomes that individuals cannot accomplish alone. Even in simple groups\, cooperative behavior often depends on complementary social roles such as leaders and followers\, yet the neural computations supporting these dynamic relationships are not well understood. Our lab investigates how the brain represents social partners\, coordinates shared goals\, and flexibly allocates control across individuals. Using a new mouse paradigm that captures naturalistic leader–follower behavior during joint foraging\, we combine large-scale neural recording\, circuit perturbation\, and computational modeling to dissect the mechanisms that enable cooperative decision-making. We find that activity in the medial prefrontal cortex reflects both the individual’s role and the evolving social context\, integrating self- and partner-related information to guide coordinated action. To probe latent strategies of the animals\, we developed a multi-agent inverse reinforcement learning framework that infers the individual goals governing joint behavior\, which closely mirror and are decodable from prefrontal activity. Together\, these studies aim to reveal general principles by which distributed brain networks support higher-order social cognition and collective behavior.
URL:https://arni-institute.org/event/ctn-herbert-zheng-wu/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260206T160000
DTEND;TZID=America/New_York:20260206T170000
DTSTAMP:20260513T201737
CREATED:20260204T163514Z
LAST-MODIFIED:20260204T163514Z
UID:2335-1770393600-1770397200@arni-institute.org
SUMMARY:ARNI Biological Learning Working Group
DESCRIPTION:1) Charlotte onboarded vision and audio MNIST dataloaders. She’s focusing on predictive coding for sequential tasks (e.g.\, audio data/moving MNIST).\n2) Todd built a multi-modal predictive coding baselines showing that unsupervised representation learning is possible here.\n3) Eivinas proposed a backprop based autoencoder and Hebbian based autoencoder (maybe Exponentiated Gradients?).\n4) Nihal offered to onboard 3D MNIST and workin Hebbian learning rules. \nThere was also discussion of software engineering conventions (e.g.\, Github practices\, configuration tooling\, etc.). \nVirtual Link: request @ arni@columbia.edu
URL:https://arni-institute.org/event/arni-biological-learning-working-group-6/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260213T113000
DTEND;TZID=America/New_York:20260213T130000
DTSTAMP:20260513T201737
CREATED:20260211T195441Z
LAST-MODIFIED:20260211T201644Z
UID:2351-1770982200-1770987600@arni-institute.org
SUMMARY:CTN: SueYeon Chung
DESCRIPTION:SueYeon Chung \nTitle: Computing with Neural Manifolds: A Multi-Scale Framework for Understanding Biological and Artificial Neural Networks \nAbstract: Recent breakthroughs in experimental neuroscience and machine learning have opened new frontiers in understanding the computational principles governing neural circuits and artificial neural networks (ANNs). Both biological and artificial systems exhibit an astonishing degree of orchestrated information processing capabilities across multiple scales – from the microscopic responses of individual neurons to the emergent macroscopic phenomena of cognition and task functions. At the mesoscopic scale\, the structures of neuron population activities manifest themselves as neural representations. Neural computation can be viewed as a series of transformations of these representations through various processing stages of the brain. The primary focus of my lab’s research is to develop theories of neural representations that describe the principles of neural coding and\, importantly\, capture the complex structure of real data from both biological and artificial systems. \nIn this talk\, I will present three related approaches that leverage techniques from statistical physics\, machine learning\, and geometry to study the multi-scale nature of neural computation. First\, I will introduce new theories based on statistical physics and convex geometry that connect complex geometric structures that arise from neural responses (i.e.\, neural manifolds) to the efficiency of neural representations in implementing a task. Second\, I will employ these theories to analyze how these representations evolve across scales\, shaped by the properties of single neurons\, learning dynamics\, and the transformations across distinct brain regions. Finally\, I will show how these insights extend efficient coding principles beyond early sensory stages\, linking representational geometry to efficient task implementations. This framework not only help interpret and compare models of brain data but also offers a principled approach to designing ANN models for higher-level vision. This perspective opens new opportunities for using neuroscience-inspired principles to guide the development of intelligent systems. \n 
URL:https://arni-institute.org/event/ctn-sueyeon-chung/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260219T150000
DTEND;TZID=America/New_York:20260219T160000
DTSTAMP:20260513T201737
CREATED:20260217T160830Z
LAST-MODIFIED:20260217T160830Z
UID:2367-1771513200-1771516800@arni-institute.org
SUMMARY:ARNI Continual Learning Working Group Meeting
DESCRIPTION:We are aiming to accelerate progress on the benchmark\, and will demo a working prototype very soon. If you are interested in contributing to our project\, we strongly encourage you to participate so that we can fix and implement our plan of action for the coming few months.
URL:https://arni-institute.org/event/arni-continual-learning-working-group-meeting/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260220T113000
DTEND;TZID=America/New_York:20260220T130000
DTSTAMP:20260513T201737
CREATED:20260217T161139Z
LAST-MODIFIED:20260220T192450Z
UID:2368-1771587000-1771592400@arni-institute.org
SUMMARY:CTN: Mitra Javadzadeh
DESCRIPTION:Title: Inter-area connectivity and the emergence of multi-timescale cortical dynamics \nAbstract: The brain generates behaviors spanning a wide range of timescales\, from rapid sensory responses to the slow integrative processes underlying cognition. How does the anatomical connectivity of the neocortex give rise to such flexible\, multi-timescale dynamics? In this talk\, I will examine how the parcellation of the neocortex into specialized areas\, coupled through reciprocal connections\, structures its dynamical landscape. \nIn Part I\, I will present experimental and modeling work combining simultaneous multi-area recordings in mouse visual cortex with focal optogenetic perturbations and biologically constrained latent circuit models. We show that reciprocal excitatory connections between primary (V1) and higher visual cortex (LM) generate an approximate line attractor in their joint dynamics. These dynamics selectively slow the decay of activity patterns that encode stimulus features consistently across areas\, promoting the gradual emergence of cross-area consensus. \nIn Part II\, I extend these findings within an analytical framework for balanced multi-area networks. We show how the structure and asymmetry of feedforward and feedback connectivity between cortical areas tune their contribution to globally consistent activity patterns. This framework makes model-free predictions on the organization of timescales across the neocortex. Furthermore\, we validate these predictions with new experiments using switchable optoGPCRs to selectively disrupt long-range cortical communication. \nTogether\, these results link anatomical connectivity to collective cortical computation\, providing a theory for how distributed brain areas reconcile information through structured multi-area dynamics.
URL:https://arni-institute.org/event/ctn-mitra-javadzadeh/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260220T160000
DTEND;TZID=America/New_York:20260220T170000
DTSTAMP:20260513T201737
CREATED:20260219T150147Z
LAST-MODIFIED:20260219T150154Z
UID:2391-1771603200-1771606800@arni-institute.org
SUMMARY:ARNI Biological Learning Working Group
DESCRIPTION:Continuation from prior meetings \nZoom Link- Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/arni-biological-learning-working-group-7/
LOCATION:Virtual
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260227T113000
DTEND;TZID=America/New_York:20260227T130000
DTSTAMP:20260513T201737
CREATED:20260217T161738Z
LAST-MODIFIED:20260224T165808Z
UID:2373-1772191800-1772197200@arni-institute.org
SUMMARY:CTN: Denise Cai
DESCRIPTION:Denise Cai \nTitle: Dynamic neural ensembles support memory stability and flexibility across the lifetime \nAbstract: Creating stable memories is critical for survival. An animal relies on past learning to navigate its environment\, avoid dangerous situations\, and find needed resources. Because the environment is dynamic\, stable memories must be updated with new information to enable responses to changing threats (a specific danger) and rewards (such as food and water). The brain circuits involved in memory and learning require both stability and flexibility. We found that traumatic experiences can alter past memories and have long-lasting changes to how future memories are encoded. This has important implications for how the brain stably stores and flexibly updates memories across the lifetime.
URL:https://arni-institute.org/event/ctn-denise-cai/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
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