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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dhoble, K. Nuntalid, N. Indiveri, G. Kasabov, N. |
| Copyright Year | 2012 |
| Description | Author affiliation: Knowledge Engineering & Discovery Research Institute (KEDRI), Auckland University of Technology, New Zealand (Dhoble, K.; Nuntalid, N.; Kasabov, N.) || Institute of Neuroinformatics (INI), University of Zurich and ETH Zurich (UZH - ETHZ), Switzerland (Indiveri, G.) |
| Abstract | Evolving spiking neural networks (eSNN) are computational models that evolve new spiking neurons and new connections from incoming data to learn patterns from them in an on-line mode. With the development of new techniques to capture spatio- and spectro-temporal data in a fast on-line mode, using for example address event representation (AER) such as the implemented one in the artificial retina and the artificial cochlea chips, and with the available SNN hardware technologies, new and more efficient methods for spatio-temporal pattern recognition (STPR) are needed. The paper introduces a new eSNN model dynamic eSNN (deSNN), that utilises both rank-order spike coding (ROSC), also known as time to first spike, and temporal spike coding (TSC). Each of these representations are implemented through different learning mechanisms - RO learning, and temporal spike learning - spike driven synaptic plasticity (SDSP) rule. The deSNN model is demonstrated on a small scale moving object classification problem when AER data is collected with the use of an artificial retina camera. The new model is superior in terms of learning time and accuracy for learning. It makes use of the order of spikes input information which is explicitly present in the AER data, while a temporal spike learning rule accounts for any consecutive spikes arriving on the same synapse that represent temporal components in the learned spatio-temporal pattern. |
| Starting Page | 1 |
| Ending Page | 7 |
| File Size | 2489910 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781467314886 |
| ISSN | 21614393 |
| e-ISBN | 9781467314909 |
| e-ISBN | 9781467314893 |
| DOI | 10.1109/IJCNN.2012.6252439 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neurons Encoding Training Retina Brain models Biological system modeling |
| Content Type | Text |
| Resource Type | Article |
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