@article {69228, title = {Unsupervised pretraining in biological neural networks}, journal = {Nature}, year = {2025}, month = {2025 Jun 18}, 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.

Preprint:\ https://www.biorxiv.org/content/early/2024/02/27/2024.02.25.581990

}, doi = {10.1038/s41586-025-09180-y}, url = {https://doi.org/10.1038/s41586-025-09180-y}, author = {Lin Zhong and Scott Baptista and Rachel Gattoni and Jon Arnold and Daniel Flickinger and Carsen Stringer and Marius Pachitariu} } @article {68773, title = {Meta-learning in head fixed mice navigating in virtual reality: A Behavioral Analysis}, journal = {bioRxiv}, year = {2023}, month = {2023 May 02}, abstract = {

Animals can learn general task structures and use them to solve new problems with novel sensory specifics. This capacity of {\textquoteleft}learning to learn{\textquoteright}, or meta-learning, is difficult to achieve in artificial systems, and the mechanisms by which it is achieved in animals are unknown. As a step toward enabling mechanistic studies, we developed a behavioral paradigm that demonstrates meta-learning in head-fixed mice. We trained mice to perform a two-alternative forced-choice task in virtual reality (VR), and successively changed the visual cues that signaled reward location. Mice showed increased learning speed in both cue generalization and serial reversal tasks. During reversal learning, behavior exhibited sharp transitions, with the transition occurring earlier in each successive reversal. Analysis of motor patterns revealed that animals utilized similar motor programs to execute the same actions in response to different cues but modified the motor programs during reversal learning. Our study demonstrates that mice can perform meta-learning tasks in VR, thus opening up opportunities for future mechanistic studies.

}, doi = {10.1101/2023.05.01.538936}, url = {https://doi.org/10.1101/2023.05.01.538936}, author = {Xinyu Zhao and Rachel Gattoni and Andrea Kozlosky and Angela Jacobs and Colin Morrow and Sarah Lindo and Nelson Spruston} }