Constructing artificial neural networks using genetic circuits to realize neuromorphic computing

Abstract

With the advent of the era of big data, existing computing systems limit the development of new technologies such as cloud computing and artificial intelligence.As the demand for high-performance computing continues to grow, traditional computing models are facing unprecedented challenges. Neural mimicry computing based on artificial neural networks provides a potential solution, and biological computing with advantages such as low energy consumption parallelization is very important for its research, in which gene circuitry will be the key to the construction of artificial neural networks. In this study, we used biological elements to construct gene circuits to form neural network structures to realize neural mimicry calculations.Based on the similarity between the structure of gene circuits and neural networks, we A c c e p t e d

SemiSynBio: A new era for neuromorphic computing

“…DNA and enzyme reactions are mostly irreversible reactions, leading to logic gates incapable of repeatedly opening and closing, and this issue fundamentally limits our ability to extend a DNA computing circuit from a single layer to a multilayer structure. To solve the problem of irreversible reactions, Teng et al abandoned the traditional DNA-based approach and instead chose to use genetic circuits to construct ANNs [ 51 ]. Their results showed that neuromorphic computing was constructed for the first time by using genetic circuits, realizing linear classifications, nonlinear classifications, and pattern classifications in Fig.…”