Engineering reaction-diffusion networks with properties of neural tissue

TL;DR

This paper introduces a versatile experimental and theoretical framework for designing and analyzing reaction-diffusion networks of chemical reactors that mimic neural tissue properties, enabling the creation of complex patterns like central pattern generators.

Contribution

It develops microfluidic fabrication techniques and a reaction-diffusion model to construct customizable chemical networks with neural tissue-like dynamics.

Findings

Successfully created chemical networks with CPG-like complexity

Demonstrated control over network topology and coupling strength

Validated theoretical models with experimental results

Abstract

We present an experimental system of networks of coupled non-linear chemical reactors, which we theoretically model within a reaction-diffusion framework. The networks consist of patterned arrays of diffusively coupled nanoliter-scale reactors containing the Belousov- Zhabotinsky (BZ) reaction. Microfluidic fabrication techniques are developed that provide the ability to vary the network topology, the reactor coupling strength and offer the freedom to choose whether an arbitrary reactor is inhibitory or excitatory coupled to its neighbor. This versatile experimental and theoretical framework can be used to create a wide variety of chemical networks. Here we design, construct and characterize chemical networks that achieve the complexity of central pattern generators (CPGs), which are found in the autonomic nervous system of a variety of organisms.