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  • AI and Neuroscience/Cognitive Science Activities Brainstorming

    ARNI will host an informal brainstorming session on July 15th (ZI Education Lab) focused on developing AI and neuroscience/cognitive science activities for K–12 students. The goal is to create engaging ways to help young learners better understand the brain and artificial intelligence. Trainees are encouraged to attend—if you're interested in making an impact on youth…

  • 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…

  • 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…

  • 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…

  • CTN: Dani Bassett

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

    Title and Abstract: TBD Zoom: Meeting ID: 993 3345 6502 Passcode: Upon request @ [email protected]

  • CTN: Ann Kennedy

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

    Title: Neural computations underlying the regulation of motivated behavior Abstract: As we interact with the world around us, we experience a constant stream of sensory inputs, and must generate a constant stream…

  • ARNI Continual Learning Working Group Project

    CEPSR 620 Schapiro 530 W. 120th St

    Next Monday, September 29, we will continue our fall semester program with a group workshop on the topic of neural memory models.  First, we will have a presentation from group…