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  • Saturday Science

    JLGSC 605 W 129th Street

    If you have children or know any young people interested in hands-on STEM fun, bring them to Saturday Science, hosted by Columbia’s CUNO group! The event takes place on Saturday, June 7th at the Jerome L. Greene Science Center (605 West 129th Street). Kids will enjoy engaging, interactive activities while exploring exciting science concepts. ARNI…

  • 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

  • Speaker: Dr. Guillaume Lajoie – ARNI Frontier Models for Neuroscience and Behavior Working Group

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

    Title: POSSM: Generalizable, real-time neural decoding with hybrid state-space models Abstract:  Real-time decoding of neural spiking data is a core aspect of neurotechnology applications such as brain-computer interfaces, where models are subject to strict latency constraints. Traditional methods, including simple recurrent neural networks, are fast and lightweight but are less equipped for generalization to unseen…

  • 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…

  • 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…