Abstract：
:A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity and policy dependence in place cells suggests that the representation is not purely spatial. We approach this puzzle from a reinforcement learning perspective: what kind of spatial representation is most useful for maximizing future reward? We show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. Furthermore, we argue that entorhinal grid cells encode a low-dimensionality basis set for the predictive representation, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.
journal_name
Nat Neuroscijournal_title
Nature neuroscienceauthors
Stachenfeld KL,Botvinick MM,Gershman SJdoi
10.1038/nn.4650subject
Has Abstractpub_date
2017-11-01 00:00:00pages
1643-1653issue
11eissn
1097-6256issn
1546-1726pii
nn.4650journal_volume
20pub_type
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