Abstract
Artificial neural networks, also known as connectionist systems, have now a long history as major techniques in computational intelligence with a wide range of application for learning from data and for artificial intelligence (AI). The main focus of the chapter is a class of ANN, called evolving connectionist systems (ECOS) that evolve their structure and functionality through incremental and life-long learning from data. Methods, systems and a wide range of applications of ECOS are presented and referenced. ECOS principles are further used for the development of brain-inspired evolving spiking neural network architectures (BI-eSNN). An example is the NeuCube architecture, which is illustrated with deep learning algorithms and with two areas of applications for life-long learning of brain data and predictive modeling of seismic data. The chapter concludes that the BI-eSNN architectures are good candidates for the future development of life-long learning and open AI systems.
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