Towards synthetic neural networks: Can artificial electrochemical neurons be coupled with artificial memristive synapses?

TL;DR

This paper explores the integration of artificial electrochemical neurons with memristive synapses to develop more brain-like neuromorphic systems capable of efficient data processing beyond traditional architectures.

Contribution

It introduces a novel approach combining electrochemical neurons with memristive synapses, advancing the development of brain-inspired neuromorphic hardware.

Findings

Successful coupling of electrochemical neurons with memristive synapses

Demonstration of neuromorphic functionalities in hybrid systems

Potential for scalable, brain-like computing architectures

Abstract

The enormous amount of data generated nowadays worldwide is increasingly triggering the search for unconventional and more efficient ways of processing and classifying information, eventually able to transcend the conventional von-Neumann-Turing computational central dogma. It is, therefore, greatly appealing to draw inspiration from less conventional but computationally more powerful systems such as the neural architecture of the human brain. This neuromorphic route has the potential to become one of the most influential and long-lasting paradigms in the field of unconventional computing. The material-based workhorse for current hardware platforms is largely based on standard CMOS technologies, intrinsically following the above mentioned von-Neumann-Turing prescription; we do know, however, that the brain hardware operates in a massively parallel way through a densely interconnected physical network of neurons. This requires challenging the intrinsic definition of the single units and the architecture of computing machines. (...)