Cognition without neurons: modelling anticipation in a basal reservoir computer

TL;DR

This paper introduces a biologically inspired reservoir model that demonstrates temporal anticipation without neural components, relying on local energy regulation and internal dynamics to predict periodic events.

Contribution

The authors present a minimal, neuron-free reservoir model that internally learns and re-enacts temporal patterns through homeodynamic regulation, without supervised training.

Findings

Model exhibits spontaneous re-enactment of learned periodic signals

Anticipation emerges from local energy regulation and internal dynamics

Demonstrates a potential mechanism for memory in non-neural organisms

Abstract

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.