Exploiting a Stimuli Encoding Scheme of Spiking Neural Networks for Stream Learning
Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios. One of the most promising techniques in stream learn- ing is the Spiking Neural Network, and some of them use an interesting population encod- ing scheme to transform the incom...
| Authors: | , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2019 |
| Country: | España |
| Institution: | Basque Center for Applied Mathematics (BCAM) |
| Repository: | BIRD. BCAM's Institutional Repository Data |
| OAI Identifier: | oai:bird.bcamath.org:20.500.11824/1163 |
| Online Access: | http://hdl.handle.net/20.500.11824/1163 https://doi.org/10.1016/j.neunet.2019.11.021 |
| Access Level: | Open access |
| Keyword: | Stream learning Gaussian receptive fields Population encoding Evolving Spiking Neural Networks |
| Summary: | Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios. One of the most promising techniques in stream learn- ing is the Spiking Neural Network, and some of them use an interesting population encod- ing scheme to transform the incoming stimuli into spikes. This study sheds lights on the key issue of this encoding scheme, the Gaussian receptive fields, and focuses on applying them as a pre-processing technique to any dataset in order to gain representativeness, and to boost the predictive performance of the stream learning methods. Experiments with synthetic and real data sets are presented, and lead to confirm that our approach can be applied successfully as a general pre-processing technique in many real cases. |
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