@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.} } @article {47703, title = {Variance predicts salience in central sensory processing.}, journal = {eLife}, volume = {3}, year = {2014}, month = {2014}, abstract = {

Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime--when sampling limitations constrain performance--efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability.

}, keywords = {Humans, Sensory Receptor Cells, Visual Cortex, Visual Perception}, issn = {2050-084X}, doi = {10.7554/eLife.03722}, author = {Hermundstad, Ann M and Briguglio, John J and Conte, Mary M and Victor, Jonathan D and Balasubramanian, Vijay and Tka{\v c}ik, Ga{\v s}per} }