Automatic and customizable pipeline for creating a CNN + light GBM model to predict whiskers contacting objects
WhACC is a tool for automated touched image classification.
Many neuroscience labs (e.g. Hires Lab) use tasks that involve whisker active touch against thin movable poles to study diverse questions of sensory and motor coding. Since neurons operate at temporal resolutions of milliseconds, determining precise whisker contact periods is essential. Yet, accurately classifying the precise moment of touch is time-consuming and labor intensive.
Single example trial lasting 4 seconds. Example video (left) along with whisker traces, decomposed components, and spikes recorded from L5 (right). How do we identify the precise millisecond frame when touch occurs?
Original 2048 output features extracted from the penultimate layer of the initial ResNet50 V2 model, clustered for emphasize
WhACC code and software was originally developed by Phillip Maire and Jonathan Cheung in the laboratory of Samuel Andrew Hires.
FAQs
Automatic and customizable pipeline for creating a CNN + light GBM model to predict whiskers contacting objects
We found that whacc demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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