9.29 Introduction to Computational Neuroscience (2004)
Added almost 5 years ago | 01:18:00 | 6517 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 almost 5 years ago | 01:21:00 | 4962 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 almost 5 years ago | 01:06:00 | 5612 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 almost 5 years ago | 01:18:00 | 3903 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 almost 5 years ago | 01:18:00 | 3671 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)
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