Name
Title | ||
|---|---|---|
Reservoir-based evolving spiking neural network for spatio-temporal pattern recognition |
Abstract | ||
|---|---|---|
Evolving spiking neural networks (eSNN) are computational models that are trained in an one-pass mode from streams of data. They evolve their structure and functionality from incoming data. The paper presents an extension of eSNN called reservoir-based eSNN (reSNN) that allows efficient processing of spatio-temporal data. By classifying the response of a recurrent spiking neural network that is stimulated by a spatio-temporal input signal, the eSNN acts as a readout function for a Liquid State Machine. The classification characteristics of the extended eSNN are illustrated and investigated using the LIBRAS sign language dataset. The paper provides some practical guidelines for configuring the proposed model and shows a competitive classification performance in the obtained experimental results. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24958-7_19 | ICONIP |
Keywords | Field | DocType |
spatio-temporal pattern recognition,spatio-temporal data,reservoir-based esnn,neural network,extended esnn,spiking neural network,classification characteristic,spatio-temporal input signal,incoming data,esnn act,competitive classification performance,spiking neural networks | Evolving systems,Pattern recognition,Computer science,Computational model,Sign language,Liquid state machine,Artificial intelligence,Spiking neural network,Machine learning | Conference |
Volume | ISSN | Citations |
7063 | 0302-9743 | 3 |
PageRank | References | Authors |
0.43 | 16 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan Schliebs | 1 | 380 | 18.56 |
| Haza Nuzly Abdull Hamed | 2 | 33 | 4.21 |
| Nikola K Kasabov | 3 | 3645 | 290.73 |