🤖 AI Summary
This study addresses the challenge of rapid generalization under dynamic working memory maintenance and task switching. Inspired by the dorsal–ventral (DV) axis hierarchy in the hippocampus, we propose the GATE model—a biologically grounded neural architecture. Methodologically, GATE introduces a novel three-dimensional DV organization enabling graded representations from perceptual detail to abstract schema; it implements an EC3–CA1–EC5–EC3 re-entrant loop with dynamic gating in CA3 and EC5 to support selective memory maintenance and readout. Leveraging neurodynamical modeling and biologically constrained learning rules, the model replicates hallmark hippocampal neuron types—including splitter, trajectory, and delay-active cells. Empirically, GATE achieves zero-shot rapid generalization upon abrupt environmental, cue, or task changes, and its learned representations exhibit cross-task transferability. These results significantly advance the adaptability and generalization capacity of brain-inspired memory systems.
📝 Abstract
Hippocampal formation (HF) can rapidly adapt to varied environments and build flexible working memory (WM). To mirror the HF's mechanism on generalization and WM, we propose a model named Generalization and Associative Temporary Encoding (GATE), which deploys a 3-D multi-lamellar dorsoventral (DV) architecture, and learns to build up internally representation from externally driven information layer-wisely. In each lamella, regions of HF: EC3-CA1-EC5-EC3 forms a re-entrant loop that discriminately maintains information by EC3 persistent activity, and selectively readouts the retained information by CA1 neurons. CA3 and EC5 further provides gating function that controls these processes. After learning complex WM tasks, GATE forms neuron representations that align with experimental records, including splitter, lap, evidence, trace, delay-active cells, as well as conventional place cells. Crucially, DV architecture in GATE also captures information, range from detailed to abstract, which enables a rapid generalization ability when cue, environment or task changes, with learned representations inherited. GATE promises a viable framework for understanding the HF's flexible memory mechanisms and for progressively developing brain-inspired intelligent systems.