Enhancing digital hardware evolvability with a neuromolecularware design: A biologically-motivated approach

Organisms have better adaptability that 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. By contrast, computers have fast processing speeds but with limited adaptability. A biologically motivated model (hardware design) that combines intra-and inter-neuronal information processing implemented with digital circuit was proposed. Pattern recognition was the present application domain. The circuit was tested with Quartus II system, a digital circuit simulation tool. The experimental result showed that the artificial neuromolecularware (ANM) 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.