Linking non-binned spike train kernels to several existing spike train metrics
| Autor: | Jan Van Campenhout, Benjamin Schrauwen |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Technology and Engineering
Quantitative Biology::Neurons and Cognition business.industry Computer science Cognitive Neuroscience Spike train Pattern recognition Hardware_PERFORMANCEANDRELIABILITY Machine learning computer.software_genre Computer Science Applications kernel methods Kernel method Artificial Intelligence Temporal resolution spiking neural networks Artificial intelligence business computer |
| Zdroj: | Proceedings of the 14th European Symposium on Artificial Neural Networks |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2006.11.017 |
| Popis: | This work presents three kernel functions that can be used as inner product operators on non-binned spike trains, allowing the use of state-of-the-art classification techniques. One of the main advantages is that this approach does not require the spike trains to be binned. Thus a high temporal resolution is preserved which is needed when temporal coding is used. The kernels are closely related to several recent and often-used spike train metrics which take into account the biological variability of spike trains. It follows that the different existing metrics are unified by the spike train kernels presented. As a test of the classification potential of the new kernel functions, a jittered spike train template classification problem is solved. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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