Natural Computing

, Volume 8, Issue 2, pp 199–218

Integrative connectionist learning systems inspired by nature: current models, future trends and challenges


DOI: 10.1007/s11047-008-9066-z

Cite this article as:
Kasabov, N. Nat Comput (2009) 8: 199. doi:10.1007/s11047-008-9066-z


The so far developed and widely utilized connectionist systems (artificial neural networks) are mainly based on a single brain-like connectionist principle of information processing, where learning and information exchange occur in the connections. This paper extends this paradigm of connectionist systems to a new trend—integrative connectionist learning systems (ICOS) that integrate in their structure and learning algorithms principles from different hierarchical levels of information processing in the brain, including neuronal-, genetic-, quantum. Spiking neural networks (SNN) are used as a basic connectionist learning model which is further extended with other information learning principles to create different ICOS. For example, evolving SNN for multitask learning are presented and illustrated on a case study of person authentification based on multimodal auditory and visual information. Integrative gene-SNN are presented, where gene interactions are included in the functioning of a spiking neuron. They are applied on a case study of computational neurogenetic modeling. Integrative quantum-SNN are introduced with a quantum Hebbian learning, where input features as well as information spikes are represented by quantum bits that result in exponentially faster feature selection and model learning. 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. They can also help to better understand complex information processes in the brain especially how information processes at different information levels interact. Open questions, challenges and directions for further research are presented.


Connectionist learning systems Artificial neural networks Spiking neural networks Evolving spiking neural networks Multiple task learning Multimodal audio-visual information processing Gene-spiking neural networks Computational neurogenetic modeling Quantum spiking neural networks 

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Knowledge Engineering and Discovery Research Institute, KEDRIAuckland University of TechnologyAucklandNew Zealand

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