1.1 Course Introduction

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Del curso dictado por University of Washington

Computational Neuroscience

408 calificaciones

University of Washington

Computational Neuroscience

408 calificaciones

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.

De la lección

Introduction & Basic Neurobiology (Rajesh Rao)

This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.

1.1 Course Introduction4:08

1.2 Computational Neuroscience: Descriptive Models11:50

1.3 Computational Neuroscience: Mechanistic and Interpretive Models12:35

Conoce a los instructores

Rajesh P. N. Rao
Professor
Computer Science & Engineering
Adrienne Fairhall
Associate Professor
Physiology and Biophysics

[MUSIC]

Hello, and a warm welcome to everyone of you from around the world.

This is week one of Computational Neuroscience, and

I'm your instructor, Rajesh Rao.

Let's begin our computational adventures with a picture.

You've probably seen a picture like this before.

Physicists tell us that this is the universe that we live in.

But I think they're mistaken.

This is the universe that we really live in.

This three pound mass of tissue inside our skull

is what allows us to perceive the world, and indeed the universe.

This amazing machine is what enables us to think, feel, act and be human.

This is what is enabling me to speak these words right now and

allowing you to listen.

And when the lecture gets boring which hopefully won't happen too often, you can

thank the same triple organ for enabling you to skip forward a few slides or

maybe doze off in the chair that you're sitting in.

Understanding how the brain does all of these things

is one of the most profound scientific mysteries of the 21st century.

In this course we'll try to unravel some of this mystery and

understand the brain using computational models.

In this course, we will cover three types of computational models.

The first kind are descriptive models.

So in this case we're interested in quantifying how neurons respond to

external stimuli, and what we get here is something called a neural encoding model.

Which quantitatively describes how every different

neuron responds to external stimuli.

The counterpart to encoding is decoding.

So in this case we're interested in extracting information from neurons that

have been recorded from the brain and then using this information for

controlling something like a prosthetic hand for example.

So this problem of decoding is extremely important

in the field of brain computer interfacing and neural prosthetics.

The second type of model that we look at are called mechanistic models.

So in this case we are interested in simulating the behavior of a single neuron

or a network of neurons on a computer.

So you might have heard about the Human Brain Project,

which is being led by Henry Markram in Europe.

And that project is an example of a computer simulation of an extremely large

network of neurons in the extreme case perhaps the entire brain on a computer.

The last type of models that they look at are called interpretive or

normative models.

So in this case we're interested in understanding why

brain circuits operate in the way that they do.

In other words we're interested in extracting some computational principles

that underlie the function of a particular brain circuit.

So we'll look at examples of all of these three types of models in the coming weeks.

Here are the two recommended textbooks for this course.

They're not required but they might be useful if you need additional information

besides what's covered in the lecture videos and the lecture slides.

The first one is Theoretical Neuroscience.

This is standard textbook in the field, and it's a book written by Peter Dayan and

Larry Abbott, two leading researchers in computational neuroscience.

The other textbook is called Tutorial on Neural Systems Modelling,

and it's by another leading researcher in the field, Thomas Anastasio.

And this book also comes with a math lab code that you might find useful

as you're exploring concepts and computations you are assigned.

So let's end with some of the goals of the course.

In other words, what can we expect to learn in this course.

Well, at the end of the course you should be able to first of all,

quantitatively describe what a biological neuron or network of neurons is doing

given some experimental data that perhaps you got from your neuroscientist friend.

Secondly, you would like to be able to simulate on a computer the behavior of

neurons or the networks or neurons.

And finally, you should be able to at the end of the course formulate computational

principle that would help explain the operation of certain neurons or

networks in the brain.

So are you ready?

Let's begin.

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