[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.
