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  • CTN: Andrew Saxe

    Zoom Link: https://columbiauniversity.zoom.us/j/92032394293?pwd=ZkQBLK7LrSU7ku2zkvXTd2QEw4WUSn.1

  • CTN: Blake Richards

    Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United States

    Title: Brain-like learning with exponentiated gradients Abstract: Computational neuroscience relies on gradient descent (GD) for training artificial neural network (ANN) models of the brain. The advantage of GD is that…

  • Speaker: Memming Park – ARNI Frontier Models for Neuroscience and Behavior Working Group

    Zuckerman Institute - L3-079 3227 Broadway, New York, NY, United States

    Title: Meta-dynamical state space modeling for integrative neural data analysis Abstract: Uncovering the organizing principles of neural systems requires integrating information across diverse datasets—each alone offering a limited view and signal-to-noise ratio, but together revealing coherent dynamical structures. We present a meta-dynamical state-space modeling framework that learns a shared solution space of neural dynamics from…

  • Speaker: Kwabena Boahen ARNI WG Multi-resource-cost optimization of neural network models

    Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United States

    Title: From 2D Chips to 3D Brains Abstract:  Artificial intelligence (AI) realizes a synaptocentric conception of the learning brain with dot-products and advances by performing twice as many multiplications every two months. But the semiconductor industry tiles twice as many multipliers on a chip only every two years. Moreover, the returns from tiling these multipliers…

  • Speakers: Vinam Arora and Ji Xia – ARNI Frontier Models for Neuroscience and Behavior Working Group

    Zuckerman Institute - L3-079 3227 Broadway, New York, NY, United States

    Title and Abstracts:  1st Speaker: Vinam Arora, UPenn Title: Know Thyself by Knowing Others: Learning Neuron Identity from Population Context Abstract: Identifying the functional identity of individual neurons is essential for interpreting circuit dynamics, yet remains a major challenge in large-scale in vivo recordings where anatomical and molecular labels are often unavailable. Here we introduce…

  • CTN: Christine Constantinople

    Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United States

    Title: Neural circuit mechanisms of value-based decision-making Abstract:  The value of the environment determines animals’ motivational states and sets expectations for error-based learning. But how are values computed? We developed a novel temporal wagering task with latent structure, and used high-throughput behavioral training to obtain well-powered behavioral datasets from hundreds of rats that learned the…

  • CTN: Naomi Leonard

    Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United States

    Title: Fast and Flexible Group Decision-Making Abstract: A wide range of animals live and move in groups. Many animals do better in groups than alone when, for example, foraging for food, migrating, and avoiding predators. A key to group success is social interaction. Less well understood is how a group, with no centralized control, is…

  • ARNI Continual Learning Working Group Project

    CSB 480 Mudd Building, 500 W 120th Street

    Monday (9/15) the Continual Learning Group will have a presentation from group member Yunfan Zhang.  Yunfan will be sharing his ongoing work on developing a continual learning benchmark based on…