提出仿海马体结构的GATE模型，实现自适应工作记忆与快速泛化。
GATE: Adaptive Learning with Working Memory by Information Gating in Multi-lamellar Hippocampal Formation
- 基于多层海马体架构，分层处理外部信息并构建内部表征。
- 学习复杂工作记忆任务后，神经元活动模式匹配实验记录中的多种细胞类型。
- 纵向梯度结构支持从具体到抽象的信息处理，适配变化环境下的快速泛化。
海马体结构（HF）能快速适应不同环境并构建灵活的工作记忆（WM）。为模拟其在泛化与工作记忆方面的机制，我们提出通用关联暂存编码模型（GATE），采用三维多层背腹侧（DV）架构，分层地从外部驱动信息中学习内部表征。每一层中，内嗅皮层3区-海马CA1区-内嗅皮层5区-内嗅皮层3区构成反馈回路，通过EC3的持续活动区分性保持信息，并由CA1神经元选择性读出。CA3和EC5进一步提供门控功能以调控上述过程。在完成复杂工作记忆任务后，GATE形成的神经表征与实验记录一致，包括分裂者、延迟期、证据、痕迹、延迟激活细胞及传统位置细胞。关键的是，其背腹侧架构能捕捉从细节到抽象的信息层级，实现线索、环境或任务改变时的快速泛化，且已学表征可继承。GATE为理解海马体灵活记忆机制提供了可行框架，并推动类脑智能系统的渐进发展。
原文摘要 · Abstract (English)
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.
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