Cognition without neurons: modelling anticipation in a basal reservoir computer

2025-05-04

AI Summary

How do non-neural organisms—such as the slime mold *Physarum polycephalum*—predict environmental periodicities without neurons, action potentials, or supervised training? Method: We propose a neuron-free reservoir model embedded on a hexagonal spatial lattice, incorporating a local allostatic energy regulation mechanism. The model relies solely on homeodynamic (i.e., internally driven, non-equilibrium) evolution to achieve self-organized temporal pattern completion. Contribution: We provide the first theoretical and experimental demonstration that pure homeodynamic regulation suffices for unsupervised temporal prediction. After exposure to periodic stimuli, the model autonomously recapitulates its dynamics in the absence of external input—exhibiting spontaneous, zero-input predictive replay. This work establishes a novel paradigm for memory and anticipation in non-neural systems and expands the theoretical foundations of biological intelligence and neuromorphic computing.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

How do non-neural organisms, such as the slime mould extit{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.

Problem

• How non-neural organisms anticipate periodic events
• Minimal reservoir model for temporal anticipation without neurons
• Simple homeodynamic regulation enabling unsupervised prediction

Innovation

• Hexagonal network with local energy regulation
• Unsupervised temporal pattern completion
• Homeodynamic regulation for anticipation