Year: 2023
Type: preprint
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.
Source: arXiv (Cornell University)
Institutions: Université de Montréal, McGill University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: Dynamics of driven networks: computation in recurrent neural circuits, G2271590008, G2621336119, Credit assignment in the neocortex, Credit assignment in the neocortex, G5517715818, A Comparative Framework for Modeling the Low-Dimensional Geometry of Neural Population States, CAREER: Building interpretable models of neural population activity through view-invariant representation learning and alignment, G814497173
OpenAlex ID: W4387947617