Cellular topographic self-organization under correlational learning

15 years 4 months ago

Download www.dice.ucl.ac.be

We consider two layered binary state neural networks in which cellular topographic self-organization occurs under correlational learning. The main result is that for separable input relations, a mapping is topographic if it is stable and vice versa.

Shouji Sakamoto, Shigeko Seki, Youichi Kobuchi

Real-time Traffic

Cellular Topographic Self-organization | ESANN 2003 | ESANN 2007 | Layered Binary State | Separable Input Relations |

claim paper

» Automatic discovery of crossfamily sequence features associated with protein function

» Discovering local patterns of co evolution computational aspects and biological examples

Post Info
More Details (n/a)

Added	31 Oct 2010
Updated	31 Oct 2010
Type	Conference
Year	2003
Where	ESANN
Authors	Shouji Sakamoto, Shigeko Seki, Youichi Kobuchi

Comments (0)

Sciweavers

Cellular topographic self-organization under correlational learning

Cellular Topographic Self-organization | ESANN 2003 | ESANN 2007 | Layered Binary State | Separable Input Relations |

Explore & Download

Productivity Tools

Sciweavers