Name
Abstract | ||
|---|---|---|
It is well known that information processing in the brain depends on neuron systems. Simple neuron systems are neural networks,
and their learning methods have been studied. However, we believe that research on large-scale neural network systems is still
incomplete. Here, we propose a learning method for millions of neurons as resources for a neuron computer. The method is a
type of recurrent path-selection, so the neural network objective must have nesting structures. This method is executed at
high speed. When information processing is executed by analogue signals, the accumulation of errors is a grave problem. We
equipped a neural network with a digitizer and AD/DA (Analogue Digital) converters constructed of neurons. They retain all
information signals and guarantee precision in complex operations. By using these techniques, we generated an image shifter
constructed of 8.6 million neurons. We believe that there is the potential to design a neuron computer using this scheme. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/BF02481461 | Artificial Life and Robotics |
Keywords | DocType | Volume |
learning �9 neuron-computer �9 large-scale neural network,neural network,information processing | Journal | 5 |
Issue | ISSN | Citations |
3 | 1614-7456 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hanxi Zhu | 1 | 0 | 1.01 |
| Tomoo Aoyama | 2 | 9 | 7.90 |
| Ikuo Yoshihara | 3 | 120 | 18.53 |