Analyzing Wet-Neuromorphic Computing Using Bacterial Gene Regulatory Neural Networks

Biocomputing envisions the development computing paradigms using biological systems, ranging from micron-level components to collections of cells, including organoids. This paradigm shift exploits hidden natural computing properties, to develop miniaturized wet-computing devices that can be deployed in harsh environments, and to explore designs of novel energy-efficient systems. In parallel, we witness the emergence of AI hardware, including neuromorphic processors with the aim of improving computational capacity. This study brings together the concept of biocomputing and neuromorphic systems by focusing on the bacterial gene regulatory networks and their transformation into Gene Regulatory Neural Networks (GRNNs). We explore the intrinsic properties of gene regulations, map this to a gene-perceptron function, and propose an application-specific sub-GRNN search algorithm that maps the network structure to match a computing problem. Focusing on the model organism Escherichia coli, the base-GRNN is initially extracted and validated for accuracy. Subsequently, a comprehensive feasibility analysis of the derived GRNN confirms its computational prowess in classification and regression tasks. Furthermore, we discuss the possibility of performing a well-known digit classification task as a use case. Our analysis and simulation experiments show promising results in the offloading of computation tasks to GRNN in bacterial cells, advancing wet-neuromorphic computing using natural cells.