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Probabilistic computational neurogenetic modeling: From Cognitive Systems to Alzheimer's Disease

IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT 2011年第4期3卷 300-311页

作者： Kasabov, Nikola K. Schliebs, Reinhard Kojima, Hiroshi Auckland Univ Technol KEDRI Auckland 1010 New Zealand Auckland Univ Technol Sch Comp & Math Sci Auckland 1010 New Zealand Univ Zurich ETH Inst Neuroinformat Zurich Switzerland Univ Leipzig Paul Flechsig Inst Brain Res D-04109 Leipzig Germany Tamagawa Univ Tokyo 1940041 Japan

The paper proposes a novel research framework for building probabilistic computational neurogenetic models (pCNGM). The pCNGM is a multilevel modeling framework inspired by the multilevel information processes in the brain. The framework comprises a set of several dynamic models, namely low (molecular) level models, a more abstract dynamic model of a protein regulatory network (PRN) and a probabilistic spiking neural network model (pSNN), all linked together. Genes/proteins from the PRN control parameters of the pSNN and the spiking activity of the pSNN provides feedback to the PRN model. The overall spatio-temporal pattern of spiking activity of the pSNN is interpreted as the highest level state of the pCNGM. The paper demonstrates that this framework can be used for modeling both artificial cognitive systems and brain processes. In the former application, the pCNGM utilises parameters that correspond to sensory elements and neuromodulators. In the latter application a pCNGM uses data obtained from relevant genes/proteins to model their dynamic interaction that matches data related to brain development, higher-level brain function or disorder in different scenarios. An exemplar case study on Alzheimer's Disease is presented. Future applications of pCNGM are discussed.

关键词： Alzheimer disease brain modeling computational neurogenetic modeling cognitive systems gene/protein regulatory network probabilistic spiking neural networks

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modeling brain dynamics using computational neurogenetic approach

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COGNITIVE NEURODYNAMICS 2008年第4期2卷 319-334页

作者： Benuskova, Lubica Kasabov, Nikola Auckland Univ Technol Knowledge Engn & Discovery Res Inst Auckland 1135 New Zealand

The paper introduces a novel computational approach to brain dynamics modeling that integrates dynamic gene-protein regulatory networks with a neural network model. Interaction of genes and proteins in neurons affects the dynamics of the whole neural network. Through tuning the gene-protein interaction network and the initial gene/protein expression values, different states of the neural network dynamics can be achieved. A generic computational neurogenetic model is introduced that implements this approach. It is illustrated by means of a simple neurogenetic model of a spiking neural network of the generation of local field potential. Our approach allows for investigation of how deleted or mutated genes can alter the dynamics of a model neural network. We conclude with the proposal how to extend this approach to model cognitive neurodynamics.

关键词： computational neurogenetic modeling Gene regulatory networks Neuroinformatics Gene expression data Local field potential

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

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Natural Computing 2009年第2期8卷 199-218页

作者： Kasabov, Nikola Knowledge Engineering and Discovery Research Institute KEDRI Auckland University of Technology Auckland New Zealand

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. © Springer Science+Business Media B.V. 2008.

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

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Neuro-, genetic-, and quantum inspired evolving intelligent systems

Neuro-, genetic-, and quantum inspired evolving intelligent ...

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International Symposium on Evolving Fuzzy Systems

作者： Kasabov, Nikola Auckland Univ Technol Knowledge Eng & Discovery Res Inst Auckland New Zealand

ISBN: (纸本)0780397185

This paper discusses opportunities and challenges for the creation of evolving artificial neural network (ANN) and more general - computational intelligence (CI) models inspired by principles at different levels of information processing in the brain - neuronal-, genetic-, and quantum, and mainly - the issues related to the integration of these principles into more powerful and accurate ANN models. A particular type of ANN, evolving connectionist systems (ECOS), is used to illustrate this approach. ECOS evolve their structure and functionality through continuous learning from data and facilitate data and knowledge integration and knowledge elucidation. ECOS gain inspiration from the evolving processes in the brain. Evolving fuzzy neural networks and evolving spiking neural networks are presented as examples. With more genetic information available now, it becomes possible to integrate the gene and the neuronal information into neuro-genetic models and to use them for a better understanding of complex brain processes. Further down in the information processing hierarchy, are the quantum processes. Quantum inspired ANN may help solve efficiently the hardest computational problems. It may be possible to integrated quantum principles into brain-gene inspired ANN models for a faster and more accurate modeling. All the topics above are illustrated with some contemporary solutions, but many more open questions and challenges are raised and directions for further research outlined.

关键词： artificial neural networks computational intelligence neuro-informatics bionformatics evolving connectionist systems gene regulatory networks computational neurogenetic modeling quantum information processing

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Bioinformatics: A knowledge engineering approach

Bioinformatics: A knowledge engineering approach

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2nd IEEE International Conference on Intelligent Systems

作者： Kasabov, N Auckland Univ Technol Knowledge Engn & Discovery Res Inst Sch Comp & Informat Sci Auckland New Zealand

ISBN: (纸本)0780382781

The paper introduces the knowledge engineering (KE) approach for the modeling and the discovery of new knowledge in Bioinformatics. This approach extends the machine learning approach with various rule extraction and other knowledge representation procedures. Examples of the KE approach, and especially of one of the recently developed techniques - evolving connectionist systems (ECOS), to challenging problems in Bioinformatics are given, that include: DNA sequence analysis, microarray gene expression profiling, protein structure prediction, finding gene regulatory networks, medical prognostic systems, computational neurogenetic modeling.

关键词： bionformatics knowledge-based neural networks evolving connectionist systems DNA analysis microarray gene expression analysis gene regulatory networks computational neurogenetic modeling

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