A Hardware Design of Neuromolecular Network with Enhanced Evolvability: A Bioinspired Approach

Abstract

Silicon-based computer systems have powerful computational capability. However, they are easy to malfunction because of a slight program error. Organisms have better adaptability than computer systems in dealing with environmental changes or noise. A close structure-function relation inherent in biological structures is an important feature for providing great malleability to environmental changes. An evolvable neuromolecular hardware motivated by some biological evidence, which integrates inter- and intraneuronal information processing, was proposed. The hardware was further applied to the pattern-recognition domain. The circuit was tested with Quartus II system, a digital circuit simulation tool. The experimental result showed that the artificial neuromolecularware exhibited a close structure-function relationship, possessed several evolvability-enhancing features combined to facilitate evolutionary learning, and was capable of functioning continuously in the face of noise.