Episodic and associative memory from spatial scaffolds in the hippocampus (Online)

Abstract

The hippocampus supports two important functions: spatial navigation and the storage of episodic memories. Yet, how these two seemingly distinct roles converge in a single circuit remains an open question. In this talk, I will present a neural model that leverages the low-dimensional attractor dynamics of spatial "grid cells" to implement associative, spatial, and episodic memory, thus unifying these distinct functions. Our model, Vector-HaSH (Vector Hippocampal Scaffolded Heteroassociative Memory) operates through a factorization of the content of memories from dynamics that generate error-correcting stable states. This leads to a graceful trade-off between number of stored items and recall detail, unlike the abrupt capacity limits found in classical Hopfield-like memory models. We find that the usage of pre-structured low-dimensional representations also enables high-capacity sequence memorization by recasting the chaining problem of high-dimensional states into one of learning low-dimensional transitions. Further, our presented approach reproduces several hippocampal experiments on spatial mapping and context-dependent representations, and provides a circuit model of the 'memory palaces' used by memory athletes. Thus, this work provides a unified framework for understanding how the hippocampus simultaneously supports spatial mapping, associative memory, and episodic memory.