Name
Abstract | ||
|---|---|---|
In order to model thinking process in human brain, it is necessary to construct neural network models handling time-varying inputs. Such networks are required to be able to retain information of their past behaviors. This motivates us to introduce a concept "stimulus-accumulation-effect." In our models, each artificial neuron accumulates past stimulus effect until it is excited by the influence of current input as well as the accumulation. This effect makes it possible for the neural networks to scan (recall) all embedded memories sequentially, and to associate temporal sequences (such as melodies) with corresponding static patterns (their images and names). |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.ins.2003.02.001 | Inf. Sci. |
Keywords | Field | DocType |
sequential pattern,embedded memories sequentially,human brain,neural network,past behavior,corresponding static pattern,associate temporal sequence,past stimulus effect,artificial neuron,neural network model,current input | Melody,Computer science,Artificial neuron,Time delay neural network,Artificial intelligence,Stimulus (physiology),Artificial neural network,Spiking neural network,Recall,Machine learning | Journal |
Volume | Issue | ISSN |
159 | 3-4 | 0020-0255 |
Citations | PageRank | References |
1 | 0.38 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taiga Yamasaki | 1 | 45 | 6.08 |
| Yoshinori Kataoka | 2 | 4 | 3.32 |
| Katsuro Kameyama | 3 | 1 | 0.38 |
| Kaoru Nakano | 4 | 138 | 164.88 |