The computational limitations of balanced networks

author: Carl van Vreeswijk, CNRS - LTCI UMR 5141 Telecom ParisTech
published: Oct. 17, 2008,   recorded: September 2008,   views: 4780
Categories

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

Computation in neural networks relies crucially on non-linearity. In neural networks in the balanced state the non-linearity of the neuronal transfer function becomes functionally unimportant. The disappearance of this non- linearity strongly limits the computational power of balanced networks. I will show in examples of balanced networks for associative memory how one can try to circumvent this limitation of balanced networks and discuss the problems with these solutions.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: