Toward an evolvable neuromolecular hardware: a hardware design for a multilevel artificial brain with digital circuits
A biologically inspired neuromolecular architecture implemented on digital circuits is proposed in this paper. Digital machines and biological systems provide different modes of information processing. The former are designed to be effectively programmable, whereas the latter have self-organizing dynamics. Previously, we developed a multilevel computer model that captures intra- and interneuronal information processing. The experimental results showed that this self-organizing model has long-term evolutionary learning capability that allows it to learn in a continuous manner, and that the function of the system changes as its structure is altered. Malleability and gradual transformability play an important role in facilitating evolutionary learning. The implementation of this model on digital circuits would allow it to perform on a real-time basis and to provide an architectural paradigm for emerging molecular or neuromolecular electronic technologies.
artificial brain evolutionary adaptability multilevel evolutionary learning evolvable hardware