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Computational Systems Neurobiology [Hardcover]

Nicolas Le Nov Re , N. Le Nov Re , N. Le Novere

RRP: £126.00
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Book Description

21 July 2012 9400738579 978-9400738577 2012
Computational neurosciences and systems biology are among the main domains of life science research where mathematical modeling made a difference. This book introduces the many different types of computational studies one can develop to study neuronal systems. It is aimed at undergraduate students starting their research in computational neurobiology or more senior researchers who would like, or need, to move towards computational approaches. Based on their specific project, the readers would then move to one of the more specialized excellent textbooks available in the field. The first part of the book deals with molecular systems biology. Functional genomics is introduced through examples of transcriptomics and proteomics studies of neurobiological interest. Quantitative modelling of biochemical systems is presented in homogeneous compartments and using spatial descriptions. A second part deals with the various approaches to model single neuron physiology, and naturally moves to neuronal networks. A division is focused on the development of neurons and neuronal systems and the book closes on a series of methodological chapters. From the molecules to the organ, thinking at the level of systems is transforming biology and its impact on society. This book will help the reader to hop on the train directly in the tank engine.

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From the book reviews:

“This is an excellent reference on mathematical neurobiology at the single level, multiple level, and biophysical level. … This is a good neural modeling book which brings to light ideas concerning Bayes Theorems and NeuroML ensembles. I highly recommend it.” (Joseph J. Grenier, Amazon.com, July, 2014)

From the Back Cover

Computational neurosciences and systems biology are among the main domains of life science research where mathematical modeling made a difference. This book introduces the many different types of computational studies one can develop to study neuronal systems. It is aimed at undergraduate students starting their research in computational neurobiology or more senior researchers who would like, or need, to move towards computational approaches. Based on their specific project, the readers would then move to one of the more specialized excellent textbooks available in the field.

 

The first part of the book deals with molecular systems biology. Functional genomics is introduced through examples of transcriptomics and proteomics studies of neurobiological interest. Quantitative modelling of biochemical systems is presented in homogeneous compartments and using spatial descriptions. A second part deals with the various approaches to model single neuron physiology, and naturally moves to neuronal networks. A division is focused on the development of neurons and neuronal systems and the book closes on a series of methodological chapters. From the molecules to the organ, thinking at the level of systems is transforming biology and its impact on society. This book will help the reader to hop on the train directly in the tank engine.


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Amazon.com: 5.0 out of 5 stars  1 review
5.0 out of 5 stars Computational Neurosciences 31 July 2014
By Joseph J Grenier - Published on Amazon.com
Format:Hardcover
Computational Neurobiology Systems

Springer New York, Berlin, Heidelberg

Joseph J Grenier MD PhD

This is an excellent reference on mathematical neurobiology at the single level, multiple level, and biophysical level. Thermodynamics, the integrate and fire single neuron model, and multineural models are discussed in detail. The Goldman, Nernst, and Hodgkin Huxley single cable equations are derived. The Boltzmann model is also discussed. 3D biophysical models of operator, integrator neuron, and populational series are well explained. This is a good neural modeling book which brings to light ideas concerning Bayes Theorems and NeuroML ensembles. I highly recommend it.
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