Name
Abstract | ||
|---|---|---|
Transient synchronization has been used as a mechanism of recognizing auditory patterns using integrate-and-fire neural networks. We first extend the mechanism to vision tasks and investigate the role of spike dependent learning. We show that such a temporal Hebbian learning rule significantly improves accuracy of detection. We demonstrate how multiple patterns can be identified by a single pattern selective neuron and how a temporal album can be constructed. This principle may lead to multidimensional memories, where the capacity per neuron is considerably increased with accurate detection of spike synchronization. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/TNN.2003.809641 | IEEE Transactions on Neural Networks |
Keywords | DocType | Volume |
Hebbian learning,computer vision,face recognition,image coding,neural nets,synchronisation,transient analysis,Hebbian learning rule,computer vision,face recognition,integrate-and-fire model,neural networks,spike synchronization detection,temporal encoding,temporal vision,transient synchrony | Journal | 14 |
Issue | ISSN | Citations |
2 | 1045-9227 | 1 |
PageRank | References | Authors |
0.37 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vasilaki, E. | 1 | 1 | 0.37 |
| Jianfeng Feng | 2 | 646 | 88.67 |
| Hilary Buxton | 3 | 491 | 135.93 |