| Autor: |
Stachenfeld KL; DeepMind, London, UK.; Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA., Botvinick MM; DeepMind, London, UK.; Gatsby Computational Neuroscience Unit, University College London, London, UK., Gershman SJ; Department of Psychology and Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA. |
| Abstrakt: |
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. |
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