Efficient Inference in Structured Spaces

Honi Sanders; Matthew Wilson; Mirko Klukas; Sugandha Sharma; Ila Fiete

doi:10.1016/j.cell.2020.11.008

Efficient Inference in Structured Spaces

Cell. 2020 Nov 25;183(5):1147-1148. doi: 10.1016/j.cell.2020.11.008.

Authors

Honi Sanders¹, Matthew Wilson², Mirko Klukas³, Sugandha Sharma³, Ila Fiete³

Affiliations

¹ Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA; Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA; Center for Brains Minds and Machines, MIT, Cambridge, MA, USA. Electronic address: honi@mit.edu.
² Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA; Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA.
³ Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA, USA.

PMID: 33242414
DOI: 10.1016/j.cell.2020.11.008

Abstract

Whittington et al. demonstrate how network architectures defined in a spatial context may be useful for inference on different types of relational knowledge. These architectures allow for learning the structure of the environment and then transferring that knowledge to allow prediction of novel transitions.

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MeSH terms

Hippocampus
Learning*
Memory*