GATE: Adaptive learning with working memory by information gating in multi-lamellar hippocampal formation

Hippocampal formation (HF) supports both the temporary maintenance of task-relevant information and rapid relearning when task structure is preserved. Here we ask what circuit mechanism can link these two functions within a single framework. We propose a model named Generalization and Associative Temporary Encoding (GATE), whose core idea is a self-gating re-entrant EC3-CA1-EC5-EC3 loop. In each lamella, EC3 provides a memory substrate, CA1 selectively reads out the retained information under CA3 gating, and EC5 feeds back to regulate the next EC3 state. Repeating this loop across dorsoventral lamellae yields representational scales that range from local cue-dependent coding to a broader task-related structure. In simple tasks, the single-lamellar model captures selective maintenance and produces place- and splitter-like CA1 activity. In more complex tasks, the multi-lamellar model develops lap, evidence, trace, and other task-relevant representations. Under structure-preserving changes in sensory coding, positional scaffold, or task parameters, the model reuses learned representations and relearns faster. GATE provides a hypothesis-generating computational framework for studying how hippocampal-like circuit motifs may support selective memory gating and structure-preserving relearning.