Speakers: Vinam Arora and Ji Xia – ARNI Frontier Models for Neuroscience and Behavior Working Group

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 NuCLR, a self-supervised framework that learns context-aware representations of neuron identity by modeling each neuron’s role within the broader population. NuCLR employs a spatiotemporal transformer that captures both within-neuron dynamics and across-neuron interactions, and is trained with a sample-wise contrastive objective that encourages stable, discriminative embeddings across time. Across multiple open-access datasets, NuCLR outperforms prior methods in both cell type and brain region classification. It enables zero-shot generalization to entirely new populations—without retraining or access to stimulus labels—offering a scalable approach for real-time, functional decoding of neuron identity across diverse experimental settings.

2nd Speaker: Ji Xia, Columbia
Title: In painting the neural picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets.
Abstract: Understanding how the brain drives memory-guided movements requires recording neural activity from the motor cortex and interconnected subcortical areas. Neuropixels probes now allow simultaneous recordings from subsets of these areas, but no single session captures all areas of interest, and different neurons are sampled from each area across sessions. This poses a key challenge: how to integrate neural data across sessions to reconstruct the complete multi-area picture. We address this with a transformer-based autoencoder that aligns neural activity into a shared latent space across sessions and animals, separately for each brain area, including those not recorded in a given session. This approach enables single-trial analysis of multi-area neural dynamics from all areas of interest. I am now working on improving this method, and will discuss both its present challenges and promising directions for future work.

Zoom: Upon request at @ [email protected].