Geometric Phase Transition Enables Extreme Hippocampal Memory Capacity
Summary
This paper reveals that superior spatial memory in food-caching birds arises from a geometric phase transition in hippocampal population activity, from disorganized 'mist' to crystalline collective coding, enabling over 100-fold higher capacity.
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Paper page - Geometric Phase Transition Enables Extreme Hippocampal Memory Capacity
Source: https://huggingface.co/papers/2605.17199
Abstract
Superior spatial memory emerges from hippocampal population geometry transitioning from disorganized to crystalline states, enabling higher capacity and stability through topological rigidity and specific neural circuit dynamics.
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 ofhippocampal population geometry-a transition from disorganized tocrystalline 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 highergeometric stability(Shesha 0.245 v 0.166) and nearly two-fold greatertemporal 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 neuronsform the spatial scaffold whileinhibitory populationscontribute orthogonal decorrelation, a circuit motif in which excitatory andinhibitory populationsoccupy 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 revealstopological rigidityas 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 169foldrepresentational redundancy: a “geometric tax” stabilizing the manifold against biological noise. These results establishgeometric stabilityas a candidate organizing principle of biological memory: evolution achieves high-capacity memory not by proliferating neurons, but by engineering the geometry of theneural codeitself.
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