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

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 heterogeneous recordings across sessions, animals, and tasks. By capturing cross-dataset similarity and variability on a low-dimensional manifold that spans a space of dynamical systems, our approach enables few-shot inference, rapid adaptation to new recordings, and discovery of latent dynamical motifs that underlie behavior. We demonstrate its utility in modeling motor cortex activity, revealing dynamics that generalize across individuals and track the change in dynamics during learning. We argue that for understanding neural computation and real-time neuroscience applications, our approach is well-suited as a foundation model for integrative neuroscience.

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