A Unified, Scalable Framework for Neural Population Decoding

TL;DR

This paper introduces a scalable neural population decoding framework using deep learning, capable of modeling large, diverse neural datasets across multiple sessions and animals, enabling rapid adaptation and few-shot learning.

Contribution

The authors present a novel architecture and training framework that unifies large-scale neural recordings into a single model, facilitating scalable, cross-session neural decoding.

Findings

Successfully trained on data from 7 primates, over 27,000 neural units, and 100 hours of recordings.

Achieved rapid adaptation and few-shot learning on new neural sessions.

Demonstrated improved decoding performance across various neural decoding tasks.

Abstract

Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we introduce a training framework and architecture designed to model the population dynamics of neural activity across diverse, large-scale neural recordings. Our method first tokenizes individual spikes within the dataset to build an efficient representation of neural events that captures the fine temporal structure of neural activity. We then employ cross-attention and a PerceiverIO backbone to further construct a latent tokenization of neural population activities. Utilizing this architecture and training framework, we construct a large-scale multi-session model trained on large datasets from seven nonhuman primates, spanning over 158 different sessions of recording from over 27,373 neural units and over 100 hours of recordings. In a number of different tasks, we demonstrate that our pretrained model can be rapidly adapted to new, unseen sessions with unspecified neuron correspondence, enabling few-shot performance with minimal labels. This work presents a powerful new approach for building deep learning tools to analyze neural data and stakes out a clear path to training at scale.