Download Calculus for Cognitive Scientists: Partial Differential by James K. Peterson PDF

By James K. Peterson

This ebook indicates cognitive scientists in education how arithmetic, desktop technology and technological know-how might be usefully and seamlessly intertwined. it's a follow-up to the 1st volumes on arithmetic for cognitive scientists, and contains the maths and computational instruments had to know how to compute the phrases within the Fourier sequence expansions that remedy the cable equation. The latter is derived from first rules via going again to mobile biology and the proper biophysics. a close dialogue of ion flow via mobile membranes, and an evidence of the way the equations that govern such ion circulation resulting in the normal brief cable equation are integrated. There also are ideas for the cable version utilizing separation of variables, besides an evidence of why Fourier sequence converge and an outline of the implementation of MatLab instruments to compute the ideas. eventually, the normal Hodgkin - Huxley version is constructed for an excitable neuron and is solved utilizing MatLab.

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Additional resources for Calculus for Cognitive Scientists: Partial Differential Equation Models (Cognitive Science and Technology)

Example text

In this chapter, we begin by discussing how to integrate functions numerically with MatLab so that we can calculate the inner product of two functions in code. In the second text, we introduced the ideas of vector spaces, linear dependence and independence of vectors. Now we apply these ideas to vector spaces of functions and finish with the Graham–Schmidt Orthogonalization process. 1 Numerical Integration b We now discuss how to approximate a f (x)dx on the interval [a, b]. These methods generate the Newton–Cotes Formulae.

We finish with the Graham–Schmidt Orthogonalization process in both theory and code. • Chapter 3: We look again at Runge–Kutta methods for solving systems of differential equations. We look closely at how the general Runge–Kutta methods are derived and finish with a discussion of an automatic step size changing method called Runge–Kutta Fehlberg. Part III: Deriving The Cable Model We now derive the basic cable equation so we can study it. We start with a discussion of basic cell biology and the movement of ions across cell membranes.

This consists of a dendrite, a cell body or soma and the axon along which the action potential moves in the nervous system. In the process, we therefore use tools at the interface between science, mathematics and computer science. We are firm believers in trying to build models with explanatory power. Hence, we abstract from biological complexity relationships which then are given mathematical form. This mathematical framework is usually not amenable to direct solution using calculus and other such tools and hence part of our solution must include simulations of our model using some sort of computer language.

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