9.29 Introduction to Computational Neuroscience (2004)
Added over 6 years ago | 01:18:00 | 7381 views
In this first lecture of the class, an overview of the course structure is provided, followed by the dual definition of Computational Neuroscience as A) using a computer to study the brain, and B) studying the brain as a computer, then a...
Added over 6 years ago | 01:21:00 | 5671 views
In this lecture, we'll learn about two mathematical operations that are commonly used in signal processing, convolution and correlation. The convolution is used to linearly filter a signal, for example to smooth a spike train to estimate...
Added over 6 years ago | 01:06:00 | 6116 views
MATLAB is a powerful software package for matrix manipulation. It’s a very useful language not only for this class, but for a variety of scientific applications, and is used widely thoughout industry. Just as when you have a hammer, ever...
Added over 6 years ago | 01:18:00 | 4678 views
In this lecture the famous Wiener-Hopf equations are introduced, and it is shown how they can be used to calculate the filter in a model of the neuron, where the output is a linear filtration of the stimulus. In addition, convolution an...
Added over 6 years ago | 01:18:00 | 4169 views
In this lecture, the discussion of the Weiner Hopf equations is continued, applying this to white noise analysis, and spike train averaging. An introduction to the basics of the visual system, and visual receptive fields is given.
- 9.29 Introduction to Computational Neuroscience (2004) (hebb.mit.edu/courses/9.29/2004/lectures/index.html)