Sleep-Deep-Net learns sleep wake scoring from the end-user and completes each record in their style.

electrophysiologic mouse data. A necessary but time-consuming step in this field is scoring epochs of recordings into wakefulness, non-rapid-eye-movement sleep and non-rapid-eye-movement sleep. Despite efforts to automate this, manual scoring remains the gold-standard since automatic methods poorly handle data that is not similar enough to data used during development. Here, we describe a novel automated sleep scoring method that involves retraining a deep-convolution-neural-net capable of computer vision to score sleep-wake patterns after learning from a small set of manual scores within a record. This avoids biasing the model to expect data to be the same as its training set from previous records.