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titlepage{
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title{
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Huge
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bf Essential Mathematics for Neuroscience}
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author{Fabian Sinz, Jakob Macke
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& Philipp Lies
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Version 0.1
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Graduate School for Neural and Behavioural Sciences
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\backslash
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T{
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"u}bingen, Germany
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\backslash
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This work is licensed under a
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Creative Commons Attribution-ShareAlike 3.0 Unported License
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%
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includegraphics[width=10cm]{./figs/Caspar_David_Friedrich_009}}
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maketitle
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Preface
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These course notes arose from a mathematics lecture for neuroscientists
that Jakob, Philipp, and me held over the course of several years at the
Graduate School for Neural and Behavioural Sciences in T
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bingen.
We used the lecture notes to collect everything the students needed to
know for solving the homework tasks in one single file.
The lecture notes are still work in progress.
Therefore, they are incomplete, contain spelling mistakes, and could be
improved in many aspects.
However, if you want to use them anyway, you are free to do so (under the
license above) as long as you acknowledge us.
If you find mistakes, we would be grateful if you would let us know so
we can correct them.
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Houston,
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today,
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Fabian Sinz
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\begin_layout Chapter
Basics
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This chapter serves as an introduction to a few basic elements that will
be needed troughout the course.
We begin by reviewing basic families of functions like
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linear
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functions
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linear functions
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,
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polynomials
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polynomials
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and
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trigonometric functions
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trigonometric functions
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, as well as some of their properties.
Afterwards we will look at some elementary calculus on those types of functions.
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\begin_layout Chapter
Linear Algebra
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Linear Algebra plays an important role in many branches of mathematics and
natural sciences.
Even when not visible at first glance, linear algebra can help you to understan
d the biggest portion of mathematics that you will ever have to deal with.
This is basically due to the fact that most of linear algebra can be interprete
d geometrically which is a helpful source of intuition.
However, linear algebra comes with the price that it is confusing for most
people at the beginning.
The reason for this is basically that linear algebra introduces a lot of
new definitions and uses a special kind of notation, the matrix notation,
for its operations.
But as soon as one got used to the linear algebra way of thinking, it is
a very helpful and natural tool which one does not want to miss.
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We will start this section by introducing
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vectors
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vectors
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which will allow us to efficiently represent
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inputs in a single variable
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Along with them we will introduce a number of useful operations that can
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we saw that a linear function is completely determined by a single input-output
pair
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It will turn out that, similar to one-dimensional linear functions, a
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input output pairs
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This will enable us to introduce matrices, a extremely powerful tool when
dealing with linear functions of more than one dimension.
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We will finish this chapter with advanced operations and properties of matrices,
such as eigenvectors and eigenvalues.
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\begin_layout Chapter
A Tiny Glimpse of Fourier Analysis
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Practially all functions can be decomposed into components of different
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looks like it mainly consists of low-frequency components, the second one
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cy component with a little bit of high-frequency stuff riding on top of
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studies how one can find mathematically decompose a function into its components
at different frequencies.
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Fourier Analysis is a very broad and deep subject with lots of lengthy books
devoted to it, so we will barely be able to cover just a tiny subsection
of it.
To understand and appreciate Fourier Analysis in its full glory, one needs
to use complex numbers, and we will not have time to look at complex numbers
in detail.
One can understand the basic concepts of Fourier Analysis without them,
so we will first explain the ideas and concepts in such a way that we can
completely avoid complex numbers.
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will be required to understand the later parts of this chapter.
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We will focus on
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deterministic
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the study of
\emph on
stochastic
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functions, i.e.
random processes.
Most practical applications are actually concerned with (possibly noisy)
data, and therefore have to deal with the stochastic case.
We will briefly mention how the concepts that we derived in the (simpler)
deterministic case can be extended to random processes.
Statistical estimation of the parameters of Fourier series from real data,
such as local field potentials, is a tricky business that requires great
care, and is beyond the scope of this course.
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Appendix
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