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

Single-Unit Activity in the Medial Prefrontal Cortex during Immediate and Delayed Extinction of Fear in Rats

Delivering extinction trials minutes after fear conditioning yields only a short-term fear suppression that fully recovers the following day. Because extinction has been reported to increase CS-evoked spike firing and spontaneous bursting in the infralimbic (IL) division of the medial prefrontal cortex (mPFC), we explored the possibility that this immediate extinction deficit is related to altered mPFC function. Single-units were simultaneously recorded in rats from neurons in IL and the prelimbic (PrL) division of the mPFC during an extinction session conducted 10 minutes (immediate) or 24 hours (delayed) after auditory fear conditioning. In contrast to previous reports, IL neurons exhibited CS-evoked responses early in extinction training in both immediate and delayed conditions and these responses decreased in magnitude over the course of extinction training. During the retention test, CS-evoked firing in IL was significantly greater in animals that failed to acquire extinction. Spontaneous bursting during the extinction and test sessions was also different in the immediate and delayed groups. There were no group differences in PrL activity during extinction or retention testing. Alterations in both spontaneous and CS-evoked neuronal activity in the IL may contribute to the immediate extinction deficit

Automated acoustic detection of mouse scratching

Itch is an aversive somatic sense that elicits the desire to scratch. In animal models of itch, scratching behavior is frequently used as a proxy for itch, and this behavior is typically assessed through visual quantification. However, manual scoring of videos has numerous limitations, underscoring the need for an automated approach. Here, we propose a novel automated method for acoustic detection of mouse scratching. Using this approach, we show that chloroquine-induced scratching behavior in C57BL/6 mice can be quantified with reasonable accuracy (85% sensitivity, 75% positive predictive value). This report is the first method to apply supervised learning techniques to automate acoustic scratch detection