A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics

DOI10.1016/J.NEUNET.2006.09.013zbMath1114.68057OpenAlexW2094730234WikidataQ45965636 ScholiaQ45965636MaRDI QIDQ872399

Brigitte Quenet, Rémi Dubois, Rémi Gervais, Gérard Dreyfus, Joëlle Haddad, Claire Martin, Francois-Benoit Vialatte

Publication date: 27 March 2007

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2006.09.013

zbMATH Keywords

neural networkwaveletbumpmachine learningtime-frequencyolfactionratelectro-encephalographylocal field potentialodour recognition

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Quantifying Statistical Interdependence, Part III: N > 2 Point Processes ⋮ Quantifying Statistical Interdependence by Message Passing on Graphs—Part II: Multidimensional Point Processes

Cites Work

• A theory for multiresolution signal decomposition: the wavelet representation
• Matching pursuits with time-frequency dictionaries
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