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…

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…

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

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…

Continuation of the prior meeting. Zoom: Upon request @[email protected]

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…

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…

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…

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…

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…

Zoom link: Upon request @[email protected]