%0 Journal Article %A Markus Frey %A Sander Tanni %A Catherine Perrodin %A Alice O’Leary %A Matthias Nau %A Jack Kelly %A Andrea Banino %A Christian F. Doeller %A Caswell Barry %T Deepinsight: a general framework for interpreting wide-band neural activity %D 2019 %R 10.1101/871848 %J bioRxiv %P 871848 %X Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings, yet their interpretation still depends on time-intensive manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of variables. Here, we developed DeepInsight - a deep-learning-framework able to decode sensory and behavioural variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviours, brain regions, and recording techniques. Critically, once trained, it can be analysed to determine elements of the neural code that are informative about a given variable. We validated this approach using data from rodent auditory cortex and hippocampus, identifying a novel representation of head direction encoded by CA1 interneurons. Thus, we present a robust, user-friendly tool for characterising and decoding neural recordings in an automated way. Code is available at https://github.com/CYHSM/DeepInsight. %U https://www.biorxiv.org/content/biorxiv/early/2019/12/11/871848.full.pdf