Networks: From Biology to Theory

By Jianfeng Feng , Jürgen Jost , Minping Qian

  • eBook Price: $99.00
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Networks: From Biology to Theory Cover Image

  • ISBN13: 978-1-8462-8485-4
  • 344 Pages
  • User Level: Science
  • Publication Date: September 4, 2007
  • Available eBook Formats: PDF

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Full Description
After over a century of research, we still do not understand the principle by which a stimulus, such as an odour or a sound, is represented by distributed neural ensembles within the brain; by which an individual is developed by the control of gene networks. While past studies have made detailed analysis of response profiles of single cells and the properties of a single gene in isolation, such techniques and approaches cannot easily address holistic issues of how large ensembles of neurons and genes can integrate information both spatially and temporally. There is little doubt that much of the info processing power of the brain or a gene network resides in the activities of co-operating and competing networks of neurons and genes and that if we unlock the principles whereby info is encoded and processed within these networks as a whole, rather than within single neurones or genes in isolation, we may be able to understand how the brain or gene network works. Our book will contribute to the development of such a study.
Table of Contents

Table of Contents

  1. Introduction.
  2. Part I: Theory.
  3. The Category of X
  4. Nets.
  5. Networks with Delays.
  6. Dynamical Networks.
  7. Part II: Applications in Neuroscience.
  8. Neuronal Computation Using High Order Statistics.
  9. Neuronal Model of Decision Making.
  10. Estimation of Spike Train Statistics in Spontaneously Active Biological neural Networks.
  11. Physiology and Related Models of Associative Visual Processing.
  12. Gestalt Formation in a Competitive Layered Neural Architecture.
  13. Part III: Applications in Bioinformatics.
  14. Regulatory Signals in Genomic Sequences.
  15. Dynamic Properties of Cell
  16. cycle and Life
  17. cycle networks in Budding Yeast.
  18. Understanding Protein
  19. protein Interactions: From Domain Level to Motif Level.
  20. An Efficient Algorithm for Deciphering Regulatory Motifs.
  21. The Stochastic Model and Metastability of Gene Network.
  22. Messenger RNA Information – Its Implication in Protein Structure Determination and Others.

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