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Fragmented Spatial Maps from Surprisal: State Abstraction and Efficient Planning

View ORCID ProfileMirko Klukas, Sugandha Sharma, YiLun Du, Tomas Lozano-Perez, Leslie Kaelbling, View ORCID ProfileIla Fiete
doi: https://doi.org/10.1101/2021.10.29.466499
Mirko Klukas
1BCS & McGovern Institute, MIT, Cambridge MA 02139, USA
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  • For correspondence: mirko.klukas@gmail.com
Sugandha Sharma
1BCS & McGovern Institute, MIT, Cambridge MA 02139, USA
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YiLun Du
2EECS & CSAIL, MIT, Cambridge MA 02139, USA
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Tomas Lozano-Perez
2EECS & CSAIL, MIT, Cambridge MA 02139, USA
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Leslie Kaelbling
2EECS & CSAIL, MIT, Cambridge MA 02139, USA
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Ila Fiete
1BCS & McGovern Institute, MIT, Cambridge MA 02139, USA
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  • ORCID record for Ila Fiete
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Abstract

When animals explore spatial environments, their representations often fragment into multiple maps. What determines these map fragmentations, and can we predict where they will occur with simple principles? We pose the problem of fragmentation of an environment as one of (online) spatial clustering. Taking inspiration from the notion of a contiguous region in robotics, we develop a theory in which fragmentation decisions are driven by surprisal. When this criterion is implemented with boundary, grid, and place cells in various environments, it produces map fragmentations from the first exploration of each space. Augmented with a long-term spatial memory and a rule similar to the distance-dependent Chinese Restaurant Process for selecting among relevant memories, the theory predicts the reuse of map fragments in environments with repeating substructures. Our model provides a simple rule for generating spatial state abstractions and predicts map fragmentations observed in electrophysiological recordings. It further predicts that there should be “fragmentation decision” or “fracture” cells, which in multicompartment environments could be called “doorway” cells. Finally, we show that the resulting abstractions can lead to large (orders of magnitude) improvements in the ability to plan and navigate through complex environments.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* mklukas{at}mit.edu, fiete{at}mit.edu

  • Updated Figure 4 (spatial cross-correlations); References added; Pointing out differences of our model to place field repetition model based on BVCs

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 17, 2022.
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Fragmented Spatial Maps from Surprisal: State Abstraction and Efficient Planning
Mirko Klukas, Sugandha Sharma, YiLun Du, Tomas Lozano-Perez, Leslie Kaelbling, Ila Fiete
bioRxiv 2021.10.29.466499; doi: https://doi.org/10.1101/2021.10.29.466499
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Fragmented Spatial Maps from Surprisal: State Abstraction and Efficient Planning
Mirko Klukas, Sugandha Sharma, YiLun Du, Tomas Lozano-Perez, Leslie Kaelbling, Ila Fiete
bioRxiv 2021.10.29.466499; doi: https://doi.org/10.1101/2021.10.29.466499

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