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  • CTN: Preeya Khanna

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

    Title: Mapping and Mending Dexterous Movement Control with Neurotechnology   Abstract: Dexterous movement is a hallmark of human motor ability, enabling us to interact skillfully with our environment. The loss of this capability due to movement disorders, such as Parkinson’s disease or stroke, strips individuals of independence and quality of life. This talk explores the…

  • ARNI Continual Learning Working Group Project

    CEPSR 620 Schapiro 530 W. 120th St

    Title: Benchmark Development for Lifelong Learning in LLMs Abstract: The ARNI Continual Learning working group continues its work towards developing a benchmark for lifelong learning in LLMs.  Discussions will be centered around learning over time as well as catastrophic forgetting in LLM post-training. Zoom link: By request

  • 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 it is effective at learning difficult tasks. However, it produces ANNs that are a poor phenomenological fit to biology, making them less relevant as models…

  • 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…

  • 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…