%Aigaion2 BibTeX export from Idiap Publications
%Thursday 02 May 2024 06:46:52 AM

@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},
}