Neural-Inspired Multi-Agent Molecular Communication Networks for Collective Intelligence

TL;DR

This paper introduces a bio-inspired decentralized molecular communication network of simple nanomachines that maximizes information transfer at a critical phase transition, enhancing collective intelligence.

Contribution

It proposes a novel collective communication paradigm inspired by biological brains, using simple agents with threshold-based firing modeled by cellular automata.

Findings

Network exhibits a second-order phase transition at a specific threshold.

Mutual information peaks at the critical transition point.

System maximizes information processing at the edge of chaos.

Abstract

Molecular Communication (MC) is a pivotal enabler for the Internet of Bio-Nano Things (IoBNT). However, current research often relies on super-capable individual agents with complex transceiver architectures that defy the energy and processing constraints of realistic nanomachines. This paper proposes a paradigm shift towards collective intelligence, inspired by the cortical networks of the biological brain. We introduce a decentralized network architecture where simple nanomachines interact via a diffusive medium using a threshold-based firing mechanism modeled by Greenberg-Hastings (GH) cellular automata. We derive fixed-point equations for steady-state populations via mean-field analysis and validate them against stochastic simulations. We demonstrate that the network undergoes a second-order phase transition at a specific activation threshold. Crucially, we show that both pairwise and collective mutual information peak exactly at this critical transition point, confirming that the system maximizes information propagation and processing capacity at the edge of chaos.