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Computational Neurogenetic Modeling: Integration of Spiking Neural Networks, Gene Networks, and Signal Processing Techniques

  • Nikola Kasabov
  • Lubica Benuskova
  • Simei Gomes Wysoski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3697)

Abstract

The paper presents a theory and a new generic computational model of a biologically plausible artificial neural network (ANN), the dynamics of which is influenced by the dynamics of internal gene regulatory network (GRN). We call this model a “computational neurogenetic model” (CNGM) and this new area of research Computational Neurogenetics. We aim at developing a novel computational modeling paradigm that can potentially bring original insights into how genes and their interactions influence the function of brain neural networks in normal and diseased states. In the proposed model, FFT and spectral characteristics of the ANN output are analyzed and compared with the brain EEG signal. The model includes a large set of biologically plausible parameters and interactions related to genes/proteins and spiking neuronal activities. These parameters are optimized, based on targeted EEG data, using genetic algorithm (GA). Open questions and future directions are outlined.

Keywords

Gene Regulatory Network Local Field Potential Signal Processing Technique Spike Neural Network Artificial Neural Network Output 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Nikola Kasabov
    • 1
  • Lubica Benuskova
    • 1
  • Simei Gomes Wysoski
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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