# 9.29 Introduction to Computational Neuroscience (2004)

# Collection videos

## Introduction

Added almost 6 years ago | 01:18:00 | 7084 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...

## Correlation and Convolution

Added almost 6 years ago | 01:21:00 | 5494 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...

## MATLAB, Statistics, and Linear Regr...

Added almost 6 years ago | 01:06:00 | 5953 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...

## Wiener-Hopf equations & Convolution...

Added almost 6 years ago | 01:18:00 | 4489 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...

## Spike Train Averaging & Basics of t...

Added almost 6 years ago | 01:18:00 | 4024 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.

# Collection details

- Category
- Education
- Language
- English
- Website
- 9.29 Introduction to Computational Neuroscience (2004) (hebb.mit.edu/courses/9.29/2004/lectures/index.html)

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