A tale of two algorithms: Structured slots explain prefrontal sequence memory and are unified with hippocampal cognitive maps - Whittington, James C.R.; Dorrell, William; Behrens, Timothy E.J.; Ganguli, Surya; El-Gaby, Mohamady (Neuron (Cambridge, Mass.), 2025)
Remembering events is crucial to intelligent behavior. Flexible memory retrieval requires a cognitive map and is supported by two key brain systems: hippocampal episodic memory (EM) and prefrontal working memory (WM). Although an understanding of EM is emerging, little is understood of WM beyond simple memory retrieval. We develop a mathematical theory relating the algorithms and representations of EM and WM by unveiling a duality between storing memories in synapses versus neural activity. This results in a formalism of prefrontal WM as structured, controllable neural subspaces (activity slots) representing dynamic cognitive maps without synaptic plasticity. Using neural networks, we elucidate differences, similarities, and trade-offs between the hippocampal and prefrontal algorithms. Lastly, we show that prefrontal representations in tasks from list learning to cue-dependent recall are unified as controllable activity slots. Our results unify frontal and temporal representations of memory and offer a new understanding for dynamic prefrontal representations of WM. •A duality between the prefrontal (working) and hippocampal (episodic) sequence memory algorithms•Mechanistic understanding of prefrontal working memory as controllable activity slots•Recurrent neural networks learn activity slots on a wide array of sequence memory tasks•Prefrontal working memory representations explained as controllable activity slots The algorithm of the prefrontal working memory system on sequence memory tasks is not well understood, whereas it is well understood for the hippocampal episodic memory system. This work shows a mathematical duality between working and episodic sequence memory. This leads to a computational understanding of sequence working memory—as structured activity slots. This theory algorithmically explains recurrent neural network (RNN) and prefrontal representations during sequence memory tasks.