Machine learning for automatic prediction of the quality of electrophysiological recordings

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

Regional R&D efficiency in Korea from static and dynamic perspectives

Regional R&D efficiency in Korea from static and dynamic perspectives, Regional Studies. Research and development (R&D) efficiency has gained great attention in regional innovation research. This study examines the R&D efficiency patterns of 15 Korean regions for 2005–09. It employs data envelopment analysis to identify the regions' R&D performances relative to the best practices from the static perspective, and the Malmquist productivity index to evaluate their changes in performance within a given timeframe, providing a dynamic perspective. The results classify the Korean regions into deteriorating, lagging and improving groups, and indicate that most regions suffer from declining R&D productivity over time because of their inability to catch up with the best practices