High-capacity flexible hippocampal associative and episodic memory enabled by prestructured “spatial” representations

Abstract

ABSTRACTHippocampal circuits in the brain enable two distinct cognitive functions: construction of spatial maps for navigation and storage of sequential episodic memories. This dual role of the hippocampus remains an enduring enigma. While there have been advances in modeling the spatial representation properties of the hippocampus, we lack good models of its role in episodic memory. Here we present a neocortical-entorhinal-hippocampal network model that exhibits high-capacity general associative memory, spatial memory, and episodic memory without the memory cliff of existing neural memory models. Instead, the circuit (which we call Vector-HaSH, Vector Hippocampal Scaffolded Heteroassociative Memory) exhibits a graceful tradeoff between number of stored items and detail, achieved by factorizing content storage from the dynamics of generating error-correcting stable states. The exponentially large space avoids catastrophic forgetting. Next, we show that pre-structured representations are an essential feature for constructing episodic memory: unlike existing episodic memory models, they enable high-capacity memorization of sequences by abstracting the chaining problem into one of learning transitions within a rigid low-dimensional grid cell scaffold. Finally, we show that previously learned spatial sequences in the form of location-landmark associations can themselves be re-usably leveraged as robust scaffolds and associated with neocortical inputs for a high-fidelity one-shot memory, providing the first circuit model of the “memory palaces” used in the striking feats of memory athletes.