Plenary Keynote 2

The talk presents theoretical foundations and practical applications of intelligent information processing systems inspired by information principles in Nature. That includes neuronal-, genetic-, and quantum information principles. First, the paper reviews the main principles of information processing at neuronal-, genetic-, and quantum information levels. Each of these levels has already inspired the creation of efficient computational AI models, such as: artificial neural networks for learning; evolutionary computation for optimization; gene and protein interaction networks; quantum computation for fast parallel processing and for associative memories. The paper reviews and extends these paradigms. Examples given include: evolving spiking neural networks, applied to adaptive multimodal audio-visual information processing; integrative computational neurogenetic models applied to modeling brain functions; quantum evolutionary algorithms for exponentially faster optimization; quantum neural networks for building exponentially larger associative memories. The new models are significantly faster in feature selection and learning and can be applied to solving efficiently NP complete biological and engineering problems for adaptive, incremental learning in a large dimensional space. They can also help to better understand complex information processes in Nature and in the brain, especially how information processes at different information levels interact, and to extend our understanding on the fundamental concept of Information. Open questions, challenges and directions for further research are presented.