@article {5134, title = {Animal-to-Animal Variability in Partial Hippocampal Remapping in Repeated Environments}, journal = {The Journal of Neuroscience}, volume = {42}, year = {2022}, month = {06/2022}, pages = {5268 - 5280}, abstract = {

Hippocampal place cells form a map of the environment of an animal. Changes in the hippocampal map can be brought about in a number of ways, including changes to the environment, task, internal state of the subject, and the passage of time. These changes in the hippocampal map have been called remapping. In this study, we examine remapping during repeated exposure to the same environment. Different animals can have different remapping responses to the same changes. This variability across animals in remapping behavior is not well understood. In this work, we analyzed electrophysiological recordings from the CA3 region of the hippocampus performed by Alme et al. (2014), in which five male rats were exposed to 11 different environments, including a variety of repetitions of those environments. To compare the hippocampal maps between two experiences, we computed average rate map correlation coefficients. We found changes in the hippocampal maps between different sessions in the same environment. These changes consisted of partial remapping, a form of remap- ping in which some place cells maintain their place fields, whereas other place cells remap their place fields. Each animal exhibited partial remapping differently. We discovered that the heterogeneity in hippocampal representational changes across animals is structured; individual animals had consistently different levels of partial remapping across a range of independent comparisons. Our findings highlight that partial hippocampal remapping between repeated environments depends on animal- specific factors.

}, keywords = {context, hippocampus, interindividual variability, overdispersion, place cell, remapping}, issn = {0270-6474}, doi = {10.1523/JNEUROSCI.3221-20.2022}, url = {https://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.3221-20.2022}, author = {Nilchian, Parsa and Matthew A. Wilson and Honi Sanders} } @article {4808, title = {Hippocampal remapping as hidden state inference}, journal = {eLife}, volume = {9}, year = {2020}, month = {06/2020}, abstract = {

Cells in the hippocampus tuned to spatial location (place cells) typically change their tuning when an animal changes context, a phenomenon known as remapping. A fundamental challenge to understanding remapping is the fact that what counts as a {\quotesinglbase}{\"A}{\`o}{\quotesinglbase}{\"A}{\`o}context change{\quotesinglbase}{\"A}{\^o}{\quotesinglbase}{\"A}{\^o} has never been precisely defined. Furthermore, different remapping phenomena have been classified on the basis of how much the tuning changes after different types and degrees of context change, but the relationship between these variables is not clear. We address these ambiguities by formalizing remapping in terms of hidden state inference. According to this view, remapping does not directly reflect objective, observable properties of the environment, but rather subjective beliefs about the hidden state of the environment. We show how the hidden state framework can resolve a number of puzzles about the nature of remapping.

}, doi = {10.7554/eLife.51140}, url = {https://elifesciences.org/articles/51140}, author = {Honi Sanders and Matthew A. Wilson and Samuel J Gershman} } @article {4298, title = {Hippocampal Remapping as Hidden State Inference}, year = {2019}, month = {08/2019}, abstract = {

Cells in the hippocampus tuned to spatial location (place cells) typically change their tuning when an animal changes context, a phenomenon known as remapping. A fundamental challenge to understanding remapping is the fact that what counts as a {\textquotedblleft}context change{\textquotedblright} has never been precisely defined. Furthermore, different remapping phenomena have been classified on the basis of how much the tuning changes after different types and degrees of context change, but the relationship between these variables is not clear. We address these ambiguities by formalizing remapping in terms of hidden state inference. According to this view, remapping does not directly reflect objective, observable properties of the environment, but rather subjective beliefs about the hidden state of the environment. We show how the hidden state framework can resolve a number of puzzles about the nature of remapping.

}, doi = {https://doi.org/10.1101/743260}, author = {Honi Sanders and Matthew A. Wilson and Samuel J Gershman} } @article {461, title = {Abstracts of the 2014 Brains, Minds, and Machines Summer Course}, number = {024}, year = {2014}, month = {09/2014}, abstract = {

A compilation of abstracts from the student projects of the 2014 Brains, Minds, and Machines Summer School, held at Woods Hole Marine Biological Lab, May 29 - June 12, 2014.

}, author = {Nadav Amir and Tarek R. Besold and Raffaello Camoriano and Goker Erdogan and Thomas Flynn and Grant Gillary and Jesse Gomez and Ariel Herbert-Voss and Gladia Hotan and Jonathan Kadmon and Scott W. Linderman and Tina T. Liu and Andrew Marantan and Joseph Olson and Garrick Orchard and Dipan K. Pal and Giulia Pasquale and Honi Sanders and Carina Silberer and Kevin A Smith and Carlos Stein N. de Briton and Jordan W. Suchow and M. H. Tessler and Guillaume Viejo and Drew Walker and Leila Wehbe and Andrei Barbu and Leyla Isik and Emily Mackevicius and Yasmine Meroz} }