@article {69577, title = {Learning reshapes the hippocampal representation hierarchy}, journal = {Proc. Natl. Acad. Sci. U.S.A.}, year = {2025}, month = {2025 Mar 10}, abstract = {

Biological neural networks seem to efficiently select and represent task-relevant features of their inputs, an ability that is highly sought after also in artificial networks. A lot of work has gone into identifying such representations in both sensory and motor systems; however, less is understood about how representations form during complex learning conditions to support behavior, especially in higher associative brain areas. Our work shows that the hippocampus maintains a robust hierarchical representation of task variables and that this structure can support new learning through minimal changes to the neural representations.

bioRxiv Preprint:\ https://www.doi.org/10.1101/2024.08.21.608911

}, doi = {10.1073/pnas.2417025122}, url = {https://doi.org/10.1073/pnas.2417025122}, author = {Chiossi, Heloisa S. C. and Nardin, Michele and Tka{\v c}ik, Ga{\v s}per and Csicsvari, Jozsef L.} }