Cognitive mapping and episodic memory emerge from simple associative learning rules

Abstract

Episodic memory enables animals to map contexts and environmental features in space and time but is underused in artificial intelligence (AI). Here we show how simple associative learning rules can be expanded to basic episodic memory in AI. We augment an agent-based foraging simulation, ASIMOV, modeled on the simple neuronal circuitry of an invertebrate forager, by adding a novel computational module for simple episodic memory, the Feature Association Matrix (FAM). The FAM is a set of computationally light, graph learning algorithms which functionally resemble the auto- and hetero-associative circuits of the hippocampus for episodic memory. In simulations, FAM enables highly efficient foraging and navigation and shows how higher-order conditioning mechanisms give rise to spatial cognitive mapping by chaining pair-wise associations and encoding them with additional contexts. Thus, FAM demonstrates a biologically inspired, bottom-up enhancement of AI for higher-order cognition.