Name
Abstract | ||
|---|---|---|
In this article, we report a simulation result of unsupervised network learning characterized as temporally and spatially local. After the learning, the network preserves an input sequence whose intervals vary in sub-millisecond order, which is much smaller than the spike emission interval of the neurons. This is achieved by the forming of systematic local structures. This formation is done by selecting appropriate connections from the input neurons and other processing neurons. As a result, the network successfully becomes a sub-millisecond temporal information processing system in a self-organizing manner. |
Year | Venue | Keywords |
|---|---|---|
1998 | ICONIP'98: THE FIFTH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING JOINTLY WITH JNNS'98: THE 1998 ANNUAL CONFERENCE OF THE JAPANESE NEURAL NETWORK SOCIETY - PROCEEDINGS, VOLS 1-3 | sub-millisecond temporal coding, unsupervised learning, network dynamics |
Field | DocType | Citations |
Computer science,Self-organizing network,Millisecond,Artificial intelligence,Artificial neural network,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.81 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ken-ichi Amemori | 1 | 7 | 2.90 |
| Shin Ishii | 2 | 532 | 43.99 |