结构化波动与生长神经细胞自动机中自我维持的信息动力学
Structured Fluctuations and the Information Dynamics of Self-Maintenance in Growing Neural Cellular Automata
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中文总结 AI 辅助
研究生长神经细胞自动机中内部波动作用，通过多种分析表明其具有空间结构且与损伤恢复相关，损伤会引发状态偏差再收敛，抑制相关更新会损害恢复，还呈现差异化修复反应及计算转变，揭示其自我修复源于高维非线性集体动力学。
中文摘要 AI 辅助
生长神经细胞自动机（GNCA）具备强大的自我维持和自我修复能力，但其内部动力学机制仍鲜为人知。本文研究了训练后的GNCA模型中内部波动（隐藏通道状态的时间微变异性）的作用，挑战了这种变异性仅是残余随机噪声的假设。通过系统分析，表明内部波动具有空间结构，与吸引性集体状态动态耦合，且与有助于损伤恢复的分布式小幅度更新相关。损伤会引发潜在状态空间的全局偏差，随后逐渐重新收敛，抑制与基线波动动力学相关的分布式小幅度更新会显著损害恢复。转移熵分析表明存在空间差异化的修复反应，部分信息分解表明恢复过程中从协同主导的静息计算向冗余增加的协调转变。这些发现表明GNCA自我修复源于高维非线性集体动力学，其中内部波动是支持信息流、协调和回归吸引性循环状态的功能组件。
英文摘要
Growing Neural Cellular Automata (GNCA) are capable of robust self-maintenance and self-repair, yet the internal dynamical mechanisms that support these capabilities remain poorly understood. Here, we investigate the role of internal fluctuations--temporal micro-variability of hidden channel states--in a trained GNCA model, challenging the assumption that such variability is merely residual stochastic noise. Through systematic analysis spanning update-rate sweeps, spatial correlation measurements, dimensionality reduction of collective state trajectories, localized damage experiments, transfer entropy vector field estimation, and partial information decomposition, we show that internal fluctuations are spatially structured, dynamically coupled to an attracting collective state, and associated with distributed small-magnitude updates that contribute to damage recovery. Damage induces a global deviation in latent state space followed by gradual re-convergence, and suppressing distributed small-magnitude updates associated with baseline fluctuation dynamics outside a permissive radius that encompasses the majority of the cells significantly impairs recovery. Transfer entropy analysis characterizes a spatially differentiated repair response: corrective inward flow near the damage site coexists with outward perturbation propagation at greater distances. Partial information decomposition further suggests a regime shift from synergy-dominant resting computation to redundancy-increased coordination during recovery. These findings indicate that GNCA self-repair emerges from high-dimensional nonlinear collective dynamics in which internal fluctuations serve as a functional component supporting information flow, coordination, and return toward an attracting recurrent state.
