Brain-, gene-, and quantum-inspired computational intelligence: Challenges and opportunities

Abstract

This paper discusses opportunities and challenges for the creation of artificial neural network (ANN) and more general - computational intelligence (CI) models inspired by principles at different levels of information processing in the brain - neuronal, genetic, and quantum, and mainly - the issues related to the integration of these principles into more powerful and accurate ANN models. A particular type of ANN, evolving connectionist systems (ECOS), is used to illustrate this approach. ECOS evolve their structure and functionality through continuous learning from data and facilitate data and knowledge integration and knowledge elucidation. ECOS gain inspiration from the evolving processes in the brain. Evolving fuzzy neural networks and evolving spiking neural networks are presented as examples. With more genetic information becoming available now, it becomes possible to integrate the gene and the neuronal information into neurogenetic models and to use them for a better understanding of complex brain processes. Further down in the information-processing hierarchy in the brain, are the quantum processes. Quantuminspired ANN may help to solve efficiently the hardest computational problems. It may be possible to integrated quantum principles into brain-gene-inspired ANN models for a faster and more accurate modeling. All the topics above are illustrated with some contemporary solutions, but many more open questions and challenges are raised and directions for further research outlined.