| Paper: | PS-2B.71 | ||
| Session: | Poster Session 2B | ||
| Location: | H Fläche 1.OG | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Hippocampal Remapping as Learned Clustering of Experience | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1129-0 | ||
| Authors: | Honi Sanders, Massachusetts Institute of Technology, United States; Samuel Gershman, Harvard University, United States; Matthew Wilson, Massachusetts Institute of Technology, United States | ||
| Abstract: | The place cells of the hippocampus collectively form distinct maps of each context, a process known as hippocampal remapping. Past work has asked which features of an experience determine which map is used, but no consistent answer has been reached. However, this approach has ignored the place of context identification as part of a learning process. We suggest that context identification corresponds to an unsupervised clustering problem, where the animal receives a stream of observations and must cluster them in a data-driven manner. Each cluster corresponds to a particular context, and therefore a particular hippocampal map. Formalizing context learning as a clustering problem allows us to capture a range of experimental results that have not yet been explained by a single theoretical framework. In particular, our results highlight the role that learning plays in hippocampal remapping. This model also provides novel predictions, such as the effect of variability in training. | ||