Hippocampal Remapping as Learned Clustering of Experiences


October 31, 2018 - 12:00pm
Northwest Building, Room 243
About the Speaker
Honi Sanders
Speaker Title: 
Postdoctoral Fellow
Speaker Affiliation: 
Gershman Lab & Wilson Lab (MIT)

Hippocampal remapping is thought to be the neural correlate of context identification.
Past work has asked which environmental features lead to remapping, but no consistent answer has been reached. However, this approach has ignored the place of context identification as part of a larger learning process.

In order to address this question, we must explicitly raise some of the complexities that make the context learning problem an intrinsically difficult problem. The animal does not know a priori what features of the environment will be relevant, nor does it have direct access to context identity labels. Fundamentally, this corresponds to an unsupervised clustering problem, where the animal receives a stream of experiences and must cluster them in a data-driven manner.

Our results emphasize that learning plays a large role in hippocampal remapping. 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. This model also provides novel predictions including the effect of variability in training as well as providing novel analyses including characterizing animal-to-animal variability.