Nature-inspired learning and adaptive systems

Abstract

Nature has been a continuous source of inspiration for many successful techniques, algorithms and computational metaphors. In this special issue, organised as part of the activities of the Nature-inspired Data Technology (NiDT) focus group within the European Co-ordinated Action project on Nature-inspired Smart Information Systems (NiSIS), we were seeking original contributions within an area of Nature-inspired technologies for Learning and Adaptive Systems. In the accepted comprehensive contributions this issue covers four quite distinctive, emerging areas inspired by broadly perceived nature and observed mechanisms and behaviours of natural systems. In the first of these contributions Kasabov discusses a new trend currently emerging from the research into extending the well established connectionist systems paradigm. As argued by the author, in contrast to a single brain-like connectionist principle of information processing exploited in the current connectionist systems, the proposed integrative connectionist learning systems (ICOS) integrate in their structure and learning algorithms principles from different hierarchical levels of information processing in the brain, including cognitive-, geneticand quantum-levels. The author further postulates that ICOS can be used to solve more efficiently challenging biological and engineering problems when fast adaptive learning systems are needed to incrementally learn in a large dimensional space. While biological metaphors and inspirations coming from the attempts to understand and model information processing, adaptation and learning mechanisms which can be