Generalization through the recurrent interaction of episodic memories: A model of the hippocampal system.

• DOI: 10.1037/a0028681
• Publication Date: 2012-07-09T17:41:36Z
• Authors: Dharshan Kumaran; James L. McClelland
• Keywords: Similarity (geometry); Neocortex

In this article, we present a perspective on the role of hippocampal system in generalization, instantiated computational model called REMERGE (recurrency and episodic memory results generalization).We expose fundamental, but neglected, tension between prevailing theories that emphasize function hippocampus pattern separation (Marr, 1971;McClelland, McNaughton, & O'Reilly, 1995), empirical support for its generalization flexible relational (Cohen Eichenbaum, 1993;Eichenbaum, 1999).Our account provides means by which to resolve conflict, demonstrating basic representational scheme envisioned complementary learning systems theory (McClelland et al., relies upon orthogonalized codes hippocampus, is compatible with efficient generalization-as long as there recurrence rather than unidirectional flow within circuit or, more widely, neocortex.We propose recurrent similarity computation, process facilitates discovery higher-order relationships set related experiences, expands scope classical exemplar-based models (e.g., Nosofsky, 1984) allows through interactions unfold dynamically created space.