Unsupervised pretraining in biological neural networks

Lin Zhong (), Scott Baptista, Rachel Gattoni, Jon Arnold, Daniel Flickinger, Carsen Stringer and Marius Pachitariu ()
Additional contact information
Lin Zhong: HHMI Janelia Research Campus
Scott Baptista: HHMI Janelia Research Campus
Rachel Gattoni: HHMI Janelia Research Campus
Jon Arnold: HHMI Janelia Research Campus
Daniel Flickinger: HHMI Janelia Research Campus
Carsen Stringer: HHMI Janelia Research Campus
Marius Pachitariu: HHMI Janelia Research Campus

Nature, 2025, vol. 644, issue 8077, 741-748

Abstract: Abstract Representation learning in neural networks may be implemented with supervised or unsupervised algorithms, distinguished by the availability of instruction. In the sensory cortex, perceptual learning drives neural plasticity1–13, but it is not known whether this is due to supervised or unsupervised learning. Here we recorded populations of up to 90,000 neurons simultaneously from the primary visual cortex (V1) and higher visual areas (HVAs) while mice learned multiple tasks, as well as during unrewarded exposure to the same stimuli. Similar to previous studies, we found that neural changes in task mice were correlated with their behavioural learning. However, the neural changes were mostly replicated in mice with unrewarded exposure, suggesting that the changes were in fact due to unsupervised learning. The neural plasticity was highest in the medial HVAs and obeyed visual, rather than spatial, learning rules. In task mice only, we found a ramping reward-prediction signal in anterior HVAs, potentially involved in supervised learning. Our neural results predict that unsupervised learning may accelerate subsequent task learning, a prediction that we validated with behavioural experiments.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41586-025-09180-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:644:y:2025:i:8077:d:10.1038_s41586-025-09180-y

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-025-09180-y

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().