Episodic and associative memory from spatial scaffolds in the hippocampus

Abstract

Hippocampal 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.

Determinantal point process attention over grid cell code supports out of distribution generalization

“…Empirical evidence suggests that, for spatial information, this is accomplished in the brain by encoding the organism’s spatial position using a periodic code consisting of different frequencies and phases (akin to a Fourier transform of the space). Although grid cells were discovered for representations of space ( Hafting et al, 2005 ; Sreenivasan and Fiete, 2011 ; Mathis et al, 2012 ) and used for guiding spatial behavior ( Erdem and Hasselmo, 2014 ; Bush et al, 2015 ), they have since been identified in non-spatial domains, such as auditory tones ( Aronov et al, 2017 ), odor ( Bao et al, 2019 ), episodic memory ( Chandra et al, 2023 ), and conceptual dimensions ( Constantinescu et al, 2016 ). These findings suggest that the coding scheme used by grid cells may serve as a general representation of metric structure that may be exploited for reasoning about the abstract conceptual dimensions required for higher-level reasoning tasks, such as analogy and mathematics ( McNamee et al, 2022 ).…”