Liquid State Machine (LSM) is a neural system based on spiking neurons that implements a mapping between functions of time. A typical application of LSM is classification of time functions obtained observing the state of the liquid by using a memoryless readout circuit, usually implemented by a linear perceptron. Due to the high number of neurons in the liquid the training of the readout is difficult. In this paper we show that using the Spike-Timing-Dependent Plasticity (STDP) a single neuron with short training session can be used to recognize the state of the liquid due to an input signal. Using STDP it is possible to identify the spikes timing of the neurons in the liquid and this allows to correctly classify a large set of input signals, the method is also robust to noise and amplitude variations
An Application of Spike-Timing-Dependent Plasticity to Readout Circuit for Liquid State Machine
Antonio Chella;Riccardo Rizzo;
2007
Abstract
Liquid State Machine (LSM) is a neural system based on spiking neurons that implements a mapping between functions of time. A typical application of LSM is classification of time functions obtained observing the state of the liquid by using a memoryless readout circuit, usually implemented by a linear perceptron. Due to the high number of neurons in the liquid the training of the readout is difficult. In this paper we show that using the Spike-Timing-Dependent Plasticity (STDP) a single neuron with short training session can be used to recognize the state of the liquid due to an input signal. Using STDP it is possible to identify the spikes timing of the neurons in the liquid and this allows to correctly classify a large set of input signals, the method is also robust to noise and amplitude variations| Campo DC | Valore | Lingua |
|---|---|---|
| dc.authority.orgunit | Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR | - |
| dc.authority.orgunit | Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR | - |
| dc.authority.people | Antonio Chella | it |
| dc.authority.people | Riccardo Rizzo | it |
| dc.authority.people | Antonio Oliveri | it |
| dc.collection.id.s | 71c7200a-7c5f-4e83-8d57-d3d2ba88f40d | * |
| dc.collection.name | 04.01 Contributo in Atti di convegno | * |
| dc.contributor.appartenenza | Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR | * |
| dc.contributor.appartenenza.mi | 860 | * |
| dc.date.accessioned | 2024/02/16 20:08:53 | - |
| dc.date.available | 2024/02/16 20:08:53 | - |
| dc.date.issued | 2007 | - |
| dc.description.abstracteng | Liquid State Machine (LSM) is a neural system based on spiking neurons that implements a mapping between functions of time. A typical application of LSM is classification of time functions obtained observing the state of the liquid by using a memoryless readout circuit, usually implemented by a linear perceptron. Due to the high number of neurons in the liquid the training of the readout is difficult. In this paper we show that using the Spike-Timing-Dependent Plasticity (STDP) a single neuron with short training session can be used to recognize the state of the liquid due to an input signal. Using STDP it is possible to identify the spikes timing of the neurons in the liquid and this allows to correctly classify a large set of input signals, the method is also robust to noise and amplitude variations | - |
| dc.description.affiliations | Universita' di Palermo; ICAR, Universita' di Palermo | - |
| dc.description.allpeople | Chella, Antonio; Rizzo, Riccardo; Oliveri, Antonio | - |
| dc.description.allpeopleoriginal | Antonio Chella; Riccardo Rizzo; Antonio Oliveri | - |
| dc.description.fulltext | none | en |
| dc.description.numberofauthors | 3 | - |
| dc.identifier.doi | 10.1109/IJCNN.2007.4371170 | - |
| dc.identifier.isbn | 978-1-4244-1379-9 | - |
| dc.identifier.scopus | ICNNF | - |
| dc.identifier.uri | https://hdl.handle.net/20.500.14243/13667 | - |
| dc.identifier.url | http://biblioproxy.cnr.it:2093/xpl/articleDetails.jsp?arnumber=4371170 | - |
| dc.language.iso | eng | - |
| dc.publisher.country | USA | - |
| dc.publisher.name | IEEE | - |
| dc.publisher.place | New York | - |
| dc.relation.conferencedate | 12 - 17 August 2007 | - |
| dc.relation.conferencename | IEEE International Joint Conference on Neural Networks | - |
| dc.relation.conferenceplace | Orlando, USA | - |
| dc.relation.firstpage | 1441 | - |
| dc.relation.ispartofbook | Proceedings of International Joint Conference on Neural Networks, 2007 | - |
| dc.relation.lastpage | 1445 | - |
| dc.relation.numberofpages | 5 | - |
| dc.subject.keywords | SPike neural netwroks | - |
| dc.subject.singlekeyword | SPike neural netwroks | * |
| dc.title | An Application of Spike-Timing-Dependent Plasticity to Readout Circuit for Liquid State Machine | en |
| dc.type.driver | info:eu-repo/semantics/conferenceObject | - |
| dc.type.full | 04 Contributo in convegno::04.01 Contributo in Atti di convegno | it |
| dc.type.miur | 273 | - |
| dc.type.referee | Sì, ma tipo non specificato | - |
| dc.ugov.descaux1 | 180406 | - |
| iris.orcid.lastModifiedDate | 2024/04/04 16:49:11 | * |
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| iris.scopus.extIssued | 2007 | - |
| iris.scopus.extTitle | An application of spike-timing-dependent plasticity to readout circuit for liquid state machine | - |
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| iris.scopus.metadataErrorType | APPLICATION | - |
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| iris.unpaywall.doi | 10.1109/ijcnn.2007.4371170 | * |
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| Appare nelle tipologie: | 04.01 Contributo in Atti di convegno | |
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