ArticleAdvances in neural information processing systems2024
Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution.
Article in Advances in neural information processing systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Article
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- Primate neuroethology: a new synthesis.Trends in cognitive sciences · 2026Review
- Exploiting correlations across trials and behavioral sessions to improve neural decoding.Neuron · 2026Article
- Article
- Map of spiking activity underlying change detection in the mouse visual system.bioRxiv : the preprint server for biology · 2025Article
- Improved inference of latent neural states from calcium imaging data.bioRxiv : the preprint server for biology · 2025Article
- Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets.ArXiv · 2025Article
- Improved interpretability in LFADS models using a learned, context-dependent per-trial bias.bioRxiv : the preprint server for biology · 2025Article
- Exploiting correlations across trials and behavioral sessions to improve neural decoding.bioRxiv : the preprint server for biology · 2025Article
- Article
- Neural Encoding and Decoding at Scale.ArXiv · 2025Article
- In vivo cell-type and brain region classification via multimodal contrastive learning.bioRxiv : the preprint server for biology · 2025Article
- A Generalist Intracortical Motor Decoder.bioRxiv : the preprint server for biology · 2025Article
- Identifying interactions across brain areas while accounting for individual-neuron dynamics with a Transformer-based variational autoencoder.Advances in neural information processing systems · 2025Article
- Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling.Advances in neural information processing systems · 2025Article
- BaRISTA: Brain Scale Informed Spatiotemporal Representation of Human Intracranial Neural Activity.Advances in neural information processing systems · 2025Article
- POCO: Scalable Neural Forecasting through Population Conditioning.Advances in neural information processing systems · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
Neuroscience research has made immense progress over the last decade, but our understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary brain region and automatically reading out the information encoded in its neural activity remains out of reach. In this work, we build towards a first foundation model for neural spiking data that can solve a diverse set of tasks across multiple brain areas. We introduce a novel self-supervised modeling approach for population activity in which the model alternates between masking out and reconstructing neural activity across different time steps, neurons, and brain regions. To evaluate our approach, we design unsupervised and supervised prediction tasks using the International Brain Laboratory repeated site dataset, which is comprised of Neuropixels recordings targeting the same brain locations across 48 animals and experimental sessions. The prediction tasks include single-neuron and region-level activity prediction, forward prediction, and behavior decoding. We demonstrate that our multi-task-masking (MtM) approach significantly improves the performance of current state-of-the-art population models and enables multitask learning. We also show that by training on multiple animals, we can improve the generalization ability of the model to unseen animals, paving the way for a foundation model of the brain at single-cell, single-spike resolution. Project page and code: https://ibl-mtm.github.io/.
Identifiers
40620317PMC12228527What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.