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DTSTART;TZID=America/New_York:20260206T113000
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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
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DTSTART;TZID=America/New_York:20260206T160000
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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/
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