9.29 Introduction to Computational Neuroscience (2004)
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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 6 years ago | 01:21:00 | 5521 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...
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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 6 years ago | 01:18:00 | 4512 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 6 years ago | 01:18:00 | 4045 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)