%Aigaion2 BibTeX export from Idiap Publications
%Thursday 02 May 2024 09:02:58 AM

@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},
}