Titlebook: Computational Electrophysiology; Shinji Doi,Junko Inoue,Kunichika Tsumoto Textbook 2010 Springer-Verlag Tokyo 2010 Neuroscience.bioinforma

gustation

A First Course in “In Silico Medicine”http://image.papertrans.cn/c/image/232269.jpg

abduction

https://doi.org/10.1007/978-3-663-04265-5systems. The dynamical system theory is related to other areas of mathematics also, for example, to the (analytic or geometric) singular perturbation theory. The singular perturbation theory is very important to analyze and understand the mathematical models in the electrophysiology (see Coombes and

placebo-effect

https://doi.org/10.1007/978-3-663-13007-9 action potential of squid giant axon, although there are still arguments against it (Connor et al. 1977; Strassberg and DeFelice 1993; Rush and Rinzel 1995; Clay 1998). The HH equations are important not only in that it is one of the most successful mathematical model in quantitatively describing b

Truculent

https://doi.org/10.1007/978-3-663-13007-9odels or abstract models. However, there is no model in which any simplifications or abstractions have not been made. Of course, many features of real neurons are ignored even in the HH equations. All models have their applicability and limits to describe natural phenomena. Therefore, all types of m

GRACE

https://doi.org/10.1007/978-3-663-13006-2e HH equations, however, include various constants or parameters whose values were determined based on physiological experiments, and thus the values inherently possess a certain ambiguity. Also, the “constants” are not really constant but change temporally. Thus, in this chapter, we study the effec

歡呼

malign

https://doi.org/10.1007/978-4-431-53862-2Neuroscience; bioinformatics; biological complexity; computational approaches to biological phenomena; d

拔出

盲信者

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