%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{paugam:esann:2007,
                      author = {Paugam-Moisy, H{\'{e}}l{\`{e}}ne and Martinez, R. and Bengio, Samy},
                    projects = {Idiap},
                       title = {A supervised learning approach based on {STDP} and polychronization in spiking neuron networks},
                   booktitle = {European Symposium on Artificial Neural Networks, {ESANN}},
                        year = {2007},
                        note = {IDIAP-RR 06-54},
                    crossref = {paugam:rr06-54},
                    abstract = {We propose a network model of spiking neurons, without preimposed topology and driven by STDP (Spike-Time-Dependent Plasticity,',','),
 a temporal Hebbian unsupervised learning mode, biologically observed. The model is further driven by a supervised learning algorithm, based on a margin criterion, that has effect on the synaptic delays linking the network to the output neurons, with classification as a goal task. The network processing and the resulting performance are completely explainable by the concept of polychronization, proposed by Izhikevich~\cite{Izh06NComp}. The model emphasizes the computational capabilities of this concept.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2007/paugam-esann-2007.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/papers/2007/paugam-esann-2007.ps.gz},
ipdmembership={learning},
}



crossreferenced publications: 
@TECHREPORT{paugam:rr06-54,
                      author = {Paugam-Moisy, H{\'{e}}l{\`{e}}ne and Martinez, R. and Bengio, Samy},
                    projects = {Idiap},
                       title = {A supervised learning approach based on {STDP} and polychronization in spiking neuron networks},
                        type = {Idiap-RR},
                      number = {Idiap-RR-54-2006},
                        year = {2006},
                 institution = {IDIAP},
                    abstract = {We propose a novel network model of spiking neurons, without preimposed topology and driven by STDP (Spike-Time-Dependent Plasticity,',','),
 a temporal Hebbian unsupervised learning mode, based on biological observations of synaptic plasticity. The model is further driven by a supervised learning algorithm, based on a margin criterion, that has effect on the synaptic delays linking the network to the output neurons, with classification as a goal task. The network processing and the resulting performance are completely explainable by the concept of polychronization, recently introduced by Izhikevich~\cite{Izh06NComp}. On the one hand, our model can be viewed as a new machine learning concept for classifying patterns by means of spiking neuron networks. On the other hand, as a model of natural neural networks, it provides a new insight on cell assemblies, a fundamental notion for understanding the cognitive processes underlying memory. {\bf Keywords.} Spiking neuron networks, Synaptic plasticity, STDP, Delay learning, Classifier, Cell assemblies, Polychronous groups.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2006/paugam-idiap-rr-06-54.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2006/paugam-idiap-rr-06-54.ps.gz},
ipdmembership={learning},
}