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 …
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 …