Language
English
Publication Date
4-1-2025
Journal
Nature
DOI
10.1038/s41586-025-08829-y
PMID
40205215
PMCID
PMC11981942
PubMedCentral® Posted Date
4-9-2025
PubMedCentral® Full Text Version
Post-print
Abstract
The complexity of neural circuits makes it challenging to decipher the brain’s algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain’s computational objectives and neural coding. However, it is difficult for such models to generalize beyond their training distribution, limiting their utility. The emergence of foundation models1 trained on vast datasets has introduced a new artificial intelligence paradigm with remarkable generalization capabilities. Here we collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. Beyond neural response prediction, the model also accurately predicted anatomical cell types, dendritic features and neuronal connectivity within the MICrONS functional connectomics dataset2. Our work is a crucial step towards building foundation models of the brain. As neuroscience accumulates larger, multimodal datasets, foundation models will reveal statistical regularities, enable rapid adaptation to new tasks and accelerate research.
Keywords
Animals, Models, Neurological, Mice, Neurons, Visual Cortex, Connectome, Male, Female, Deep Learning, Dendrites, Photic Stimulation, Extrastriate cortex, Network models, Computational models, Machine learning, Sensory processing
Published Open-Access
yes
Recommended Citation
Wang, Eric Y; Fahey, Paul G; Ding, Zhuokun; et al., "Foundation Model of Neural Activity Predicts Response to New Stimulus Types" (2025). Faculty and Staff Publications. 5323.
https://digitalcommons.library.tmc.edu/baylor_docs/5323