HIS07 Plenaries

Evolving Connectionist Systems (ECOS) are neural network systems that develop their structure, functionality and internal representation through continuous learning from data and interaction with the environment. ECOS can also evolve through generations of populations using evolutionary computation, but the focus of the presentation is on: (1) Adaptive learning and improvement of each individual model; (2) Knowledge representation, knowledge adaptation and knowledge extraction. The learning process can be: on-line, off-line, incremental, supervised, unsupervised, active, sleep/dream, etc.
Principles of different evolving processes from Nature have been used so far to build ECOS. This presentation introduces several models:

Simple evolving neural networks and evolving neuro-fuzzy systems [1];
Evolving spiking neural networks, where brain-like spiking neuronal models are used to incrementally evolve large networks [2];
Evolving gene interaction networks, that capture dynamic interaction between genes from time series genetic data [3];
Evolving neuro-genetic models, where evolving gene networks are integrated with evolving spiking neural networks to model brain data [3];
Evolving quantum-inspired neural networks, where quantum principles such as superposition and entanglement, are used to incrementally select the features and the structure of an evolving connectionist model [2];
Hybrid models, combining principles and elements from the above [2].

Different ECOS are demonstrated on challenging problems from bioinformatics, medical decision support, adaptive multimodal information processing and biometrics, autonomous robot control, environmental risk prognosis, financial on-line prediction. For some of the demonstrations, a free software environment NeuCom is used (http://www. theneucom.com).
The presentation concludes with some speculations about integrating quantum, genetic and neuronal principles in computational models, along with giving directions for further research.

References:
[1] N.Kasabov, Evolving connectionist systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines, Springer, London, 2002
[2] N.Kasabov, Evolving connectionist systems: The Knowledge Engineering Approach, (second edition) Springer, London, 2007
[3] L.Benuskova and N.Kasabov, Computational Neurogenetic Modelling, Springer, NY, 2007
Keywords: Computational intelligence, Knowledge-based neural networks, Evolving connectionist systems, Gene regulatory networks, Evolutionary computation, Quantum computation, Data mining; Knowledge discovery, Bioinformatics, Brain study, Evolving robots.

Plenary
Presenter:	Prof. Nikola Kasabov Knowledge Engineering and Discovery Research Institute (KEDRI) Auckland University of Technology, New Zealand (nkasabov@aut.ac.nz), www.kedri.info
Title:	Evolving Connectionist and Hybrid Systems : Methods, Tools, Applications
Abstract:	Evolving Connectionist Systems (ECOS) are neural network systems that develop their structure, functionality and internal representation through continuous learning from data and interaction with the environment. ECOS can also evolve through generations of populations using evolutionary computation, but the focus of the presentation is on: (1) Adaptive learning and improvement of each individual model; (2) Knowledge representation, knowledge adaptation and knowledge extraction. The learning process can be: on-line, off-line, incremental, supervised, unsupervised, active, sleep/dream, etc. Principles of different evolving processes from Nature have been used so far to build ECOS. This presentation introduces several models: Simple evolving neural networks and evolving neuro-fuzzy systems [1]; Evolving spiking neural networks, where brain-like spiking neuronal models are used to incrementally evolve large networks [2]; Evolving gene interaction networks, that capture dynamic interaction between genes from time series genetic data [3]; Evolving neuro-genetic models, where evolving gene networks are integrated with evolving spiking neural networks to model brain data [3]; Evolving quantum-inspired neural networks, where quantum principles such as superposition and entanglement, are used to incrementally select the features and the structure of an evolving connectionist model [2]; Hybrid models, combining principles and elements from the above [2]. Different ECOS are demonstrated on challenging problems from bioinformatics, medical decision support, adaptive multimodal information processing and biometrics, autonomous robot control, environmental risk prognosis, financial on-line prediction. For some of the demonstrations, a free software environment NeuCom is used (http://www. theneucom.com). The presentation concludes with some speculations about integrating quantum, genetic and neuronal principles in computational models, along with giving directions for further research. References: [1] N.Kasabov, Evolving connectionist systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines, Springer, London, 2002 [2] N.Kasabov, Evolving connectionist systems: The Knowledge Engineering Approach, (second edition) Springer, London, 2007 [3] L.Benuskova and N.Kasabov, Computational Neurogenetic Modelling, Springer, NY, 2007 Keywords: Computational intelligence, Knowledge-based neural networks, Evolving connectionist systems, Gene regulatory networks, Evolutionary computation, Quantum computation, Data mining; Knowledge discovery, Bioinformatics, Brain study, Evolving robots.
Biography:	Professor Nikola Kasabov is the Founding Director and the Chief Scientist of the Knowledge Engineering and Discovery Research Institute KEDRI, Auckland (www.kedri.info/). He holds a Chair of Knowledge Engineering at the School of Computer and Information Sciences at Auckland University of Technology. He is a Fellow of the Royal Society of New Zealand, Fellow of the New Zealand Computer Society and a Senior Member of IEEE. He is a Vice President of the International Neural Network Society (INNS), and a Past President of the Asia Pacific Neural Network Assembly (APNNA). Kasabov is on the editorial boards of 8 international journals and has been on the Program Committees of more than 50 international conferences in the last 10 years. Kasabov holds MSc and PhD from the Technical University of Sofia. His main research interests are in the areas of: neuro-computing, evolving systems, intelligent information systems, soft computing, bioinformatics, brain study, speech and image processing, novel methods for data mining and knowledge discovery. He has published more than 400 publications. He has extensive academic experience at various academic and research organisations: University of Otago, New Zealand; University of Essex, UK; University of Trento, Italy; Technical University of Sofia, Bulgaria; University of California at Berkeley; RIKEN Brain Science Institute, Tokyo; TU Kaiserslautern Germany. More information of Prof. Kasabov can be found on the Web site: http://www.kedri.info.
	UP