标题： 没有神经元的认知：基底水库计算机中的预期建模 显示英文标题

标题： Cognition without neurons: modelling anticipation in a basal reservoir computer

摘要： 如何非神经生物，如变形虫\textit{多头绒泡菌}，预测环境中的周期性事件？ 我们提出了一种最小的、生物学上受到启发的水库模型，该模型展示了没有神经元、尖峰或训练读出的简单时间预测。 该模型由一个空间嵌入的六边形网络组成，在这个网络中，节点通过局部的、同态适应来调节它们的能量。 输入扰动随着时间塑造能量动力学，使系统能够将其结构内部化为时间规律。 在暴露于周期性输入信号后，即使没有进一步的输入，该模型也会自发地重新激活那些动态——这是一种无监督的时间模式完成形式。 这种行为源自内部稳态调节，而不需要有监督学习或符号处理。 我们的结果显示，简单的稳态调节可以支持无监督预测，这为基底生物的记忆和预测提供了一条途径。 显示英文摘要

摘要： How do non-neural organisms, such as the slime mould \textit{Physarum polycephalum}, anticipate periodic events in their environment? We present a minimal, biologically inspired reservoir model that demonstrates simple temporal anticipation without neurons, spikes, or trained readouts. The model consists of a spatially embedded hexagonal network in which nodes regulate their energy through local, allostatic adaptation. Input perturbations shape energy dynamics over time, allowing the system to internalize temporal regularities into its structure. After being exposed to a periodic input signal, the model spontaneously re-enacts those dynamics even in the absence of further input -- a form of unsupervised temporal pattern completion. This behaviour emerges from internal homeodynamic regulation, without supervised learning or symbolic processing. Our results show that simple homeodynamic regulation can support unsupervised prediction, suggesting a pathway to memory and anticipation in basal organisms.