Name
Abstract | ||
|---|---|---|
Reservoir computing is a framework within which an arbitrary non-trainable reservoir, e.g. a network with a fixed structure, is used for computing, and the output of the reservoir is supplied with a simple classification or regression algorithm, which is trained to yield required results. In the current study, we investigate the possibility of a simple spiking neural network (SNN) based on the extended Brusselator model to serve as the reservoir for binary pattern recognition. We train various classifiers on data obtained from the four-neuron SNN and show that when there are two and three digits in a binary pattern three and six unique patterns can be distinguished, respectively, with the positive predictive value not less than 89 %. The obtained results can be used for developing a fast-training chemical computer taking advantage of its hardware structure. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/MECO55406.2022.9797166 | 2022 11th Mediterranean Conference on Embedded Computing (MECO) |
Keywords | DocType | ISSN |
spiking neural networks,SNN,Brusselator,clas-sification learner,reservoir computer,pattern recognition | Conference | 2377-5475 |
ISBN | Citations | PageRank |
978-1-6654-6829-9 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ekaterina Kopets | 1 | 0 | 0.68 |
| Shchetinina Tatiana | 2 | 0 | 0.34 |
| Vyacheslav Rybin | 3 | 0 | 0.68 |
| Albert Dautov | 4 | 0 | 0.68 |
| Timur Karimov | 5 | 0 | 0.68 |
| Artur Karimov | 6 | 0 | 0.68 |