Cellular topographic self-organization under correlational learning

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.

• Type: Conference
• Year: 2003
• Where: ESANN
• Authors: Shouji Sakamoto, Shigeko Seki, Youichi Kobuchi
• Keywords: Cellular Topographic Self-organization, ESANN 2003, ESANN 2007, Layered Binary State, Separable Input Relations