Machine Learning for Automatic Prediction of the Quality of Electrophysiological Recordings

Nowotny , Thomas
        . Sussex Neuroscience and Centre for computational Neuroscience and Robotics; Rospars , Jean-Pierre
        INRA
        , Versailles .
         UMR 1272 Physiologie de l'Insecte : Signalisation et Communication; Martinez , Dominique; Elbanna , Shereen
        INRA
        , Versailles .
         UMR 1272 Physiologie de l'Insecte : Signalisation et Communication; Anton , Sylvia
        INRA
        , Angers .
         USC 1330 Récepteurs et Canaux Ioniques Membranaires

Machine Learning for Automatic Prediction of the Quality of Electrophysiological Recordings

2013

Nowotny , Thomas (University of Sussex, Brighton(Royaume Uni). Sussex Neuroscience and Centre for computational Neuroscience and Robotics) | Rospars , Jean-Pierre (INRA , Versailles (France). UMR 1272 Physiologie de l'Insecte : Signalisation et Communication) | Martinez , Dominique (CNRS, Vandoeuvre-lès-Nancy Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA)) | Elbanna , Shereen (INRA , Versailles (France). UMR 1272 Physiologie de l'Insecte : Signalisation et Communication) | Anton , Sylvia (INRA , Angers (France). USC 1330 Récepteurs et Canaux Ioniques Membranaires)

The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select “good” recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80–90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters.

显示更多 [+]