Computational neuroscience: realistic modeling for by Erik De Schutter

By Erik De Schutter

Designed essentially as an creation to lifelike modeling tools, Computational Neuroscience: reasonable Modeling for Experimentalists specializes in methodological ways, making a choice on acceptable equipment, and picking out capability pitfalls. the writer addresses various degrees of complexity, from molecular interactions inside unmarried neurons to the processing of data by means of neural networks. He avoids theoretical arithmetic and gives barely enough of the fundamental math utilized by experimentalists.What makes this source special is the inclusion of a CD-ROM that furnishes interactive modeling examples. It comprises tutorials and demos, videos and photographs, and the simulation scripts essential to run the complete simulation defined within the bankruptcy examples. each one bankruptcy covers: the theoretical beginning; parameters wanted; applicable software program descriptions; review of the version; destiny instructions anticipated; examples in textual content packing containers associated with the CD-ROM; and references. the 1st e-book to carry you state of the art advancements in neuronal modeling. It presents an creation to sensible modeling tools at degrees of complexity various from molecular interactions to neural networks. The ebook and CD-ROM mix to make Computational Neuroscience: lifelike Modeling for Experimentalists the entire package deal for realizing modeling innovations.

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Wilson, M. A. and Bower, J. , The simulation of large-scale neural networks, in Methods in Neuronal Modeling: From Synapses to Networks, Koch, C. , MIT Press, Cambridge, 1989, 291. 5. Hornbeck, R. , Numerical Methods, Quantum Publishers, New York, 1975. 6. Dahlquist, G. and Björck, Å, Numerical Methods, Prentice-Hall, Englewood Cliffs, NJ, 1974. 7. Kuo, S. , Computer Application of Numerical Methods, Addison-Wesley, Reading, MA, 1972. 8. Stoer, J. , Introduction to Numerical Analysis, Springer-Verlag, New York, 1980.

It is unrealistic to suppose that one could process all these data comparisons by manual procedures. More exciting is the real perspective of having automated methods to build complete models out of detailed sets of current clamp or voltage clamp data. ), broaden these perspectives to very fast or even real time optimization and model construction. © 2001 by CRC Press LLC ACKNOWLEDGMENTS R. M. thanks Mike Wijnants for his computer assistance and Volker Steuber for his corrections and suggestions on the first part of this chapter.

20. Tabak, J. and Moore, L. , Simulation and parameter estimation study of a simple neuronal model of rhythm generation: role of NMDA and non-NMDA receptors, J. Comput. , 5, 209, 1998. © 2001 by CRC Press LLC 21. Wright, W. , Bardakjian, B. , Valiante, T. , Perez-Velazquez, J. , and Carlen, P. , White noise approach for estimating the passive electrical properties of neurons, J. , 76, 3442, 1996. 22. Foster, W. , Ungar, L. , and Schaber, J. , Significance of conductances in Hodgkin-Huxley models, J.

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