Episodic memories enable powerful algorithms for goal-directed decision making that are context-aware and explainable

Abstract

Most current methods for goal-directed action selection in the face of changing goals and contingencies require DNNs or LLMs. Therefore they are less suited for implementation in edge devices, where low energy-consumption is imperative. The brain shows that similar functionality can be produced with just 20W, even with low precision of synaptic weights. In addition, it endows our action selection with explanations in terms of prior experience, a capability that is missing in those learning approaches where prior experiences are compressed into parameter values. We show that experimental data from neuroscience and cognitive science suggest a concrete alternative where experiences are organized into cognitive maps. It only requires local online learning in shallow networks with binary synaptic weights, and provides explanations for selected actions in terms of related prior experiences. This brain-inspired approach also requires substantially fewer computation steps for navigation in abstract state spaces with deterministic or stochastic actions.