Computing of neuromorphic materials: an emerging approach for bioengineering solutions

Abstract

The potential of neuromorphic computing to bring about revolutionary advancements in multiple disciplines, such as artificial intelligence (AI), robotics, neurology, and cognitive science, is well recognised. This paper presents a comprehensive survey of current advancements in the use of machine learning techniques for the logical development of neuromorphic materials for engineering solutions. The amalgamation of neuromorphic technology and material design possesses the potential to fundamentally revolutionise the procedure of material exploration, optimise material architectures at the atomic or molecular level, foster self-adaptive materials, augment energy efficiency, and enhance the efficacy of brain–machine interfaces (BMIs). Consequently, it has the potential to bring about a paradigm shift in various sectors and generate innovative prospects within the fields of material science and engineering. The objective of this study is to advance the field of artificial intelligence (AI) by creating hardware for neural networks that is energy-efficient. Additionally, the research attempts to improve neuron models, learning algorithms, and learning rules. The ultimate goal is to bring about a transformative impact on AI and better the overall efficiency of computer systems.