几何相变赋予海马体极致的记忆容量

摘要

记忆系统能在相似的硬件限制下存储数量悬殊的信息。本文证明，优越的空间记忆源于海马群体编码发生离散性硬化——从无序到晶体化集体编码的相变。通过比较存储食物的山雀与非存储食物的斑胸草雀，我们发现存储型海马维持着拓扑刚性的"晶体化"几何结构，其几何稳定性显著更高（Shesha指数0.245 vs 0.166），时间相干性提升近两倍（Shesha指数0.393 vs 0.209），而非存储型海马则呈现类似无序"迷雾"的编码模式。这种稳定性通过协同电路动力学主动构建：兴奋性神经元搭建空间支架，与之相伴的是抑制性群体贡献正交去相关——这种电路基序中兴奋性与抑制性群体占据近乎不重叠的表征子空间。与Valiant稳定记忆分配器的双重分离实验（该模型预测每个记忆对应专用神经元集群）证实，此优势源于连续拓扑组织而非离散神经元分配：存储型网络虽具几何优越性，其分半分配信度却趋近于零。基于1万种配置的计算建模揭示，拓扑刚性是规模扩展的数学前提：晶体化编码能在超过M=1k位置时维持高保真度读取，而迷雾编码在M=10时即失效，两者容量差异超百倍。这种容量需要169倍的表征冗余——一种稳定流形对抗生物噪声的"几何税"。这些发现将几何稳定性确立为生物记忆的候选组织原则：进化实现高容量记忆并非通过增殖神经元，而是通过重新设计神经编码本身的几何结构。

English

Memory systems can store vastly different amounts of information despite similar hardware constraints. Here, we show that superior spatial memory emerges from a discrete stiffening of hippocampal population geometry-a transition from disorganized to crystalline collective coding. Comparing food-caching chickadees to non-caching zebra finches, we found that the caching hippocampus maintains a topologically rigid, "crystalline" geometry with significantly higher geometric stability (Shesha 0.245 v 0.166) and nearly two-fold greater temporal coherence (Shesha 0.393 v 0.209), while the non-caching hippocampus resembles a disorganized "mist." This stability is actively constructed by synergistic circuit dynamics: excitatory neurons form the spatial scaffold while inhibitory populations contribute orthogonal decorrelation, a circuit motif in which excitatory and inhibitory populations occupy largely non-overlapping representational subspaces. A double dissociation with Valiant's Stable Memory Allocator, a model predicting that dedicated neuron ensembles underlie each memory, confirms this advantage reflects continuous topological organization rather than discrete neuron allocation: caching networks exhibit near-zero split-half allocation reliability despite their geometric superiority. Computational modeling across 10k configurations reveals topological rigidity as the mathematical prerequisite for scale: crystalline codes sustain high-fidelity readout beyond M=1k locations while mist codes fail below M=10, a >100-fold capacity advantage. This capacity requires a 169fold representational redundancy: a "geometric tax" stabilizing the manifold against biological noise. These results establish geometric stability as a candidate organizing principle of biological memory: evolution achieves high-capacity memory not by proliferating neurons, but by engineering the geometry of the neural code itself.