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Updated: May 27, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
Published on: February 6, 2020
The surveillance state of behavioral automation
Andreas T Schaefer1, Adam Claridge-Chang
1Behavioural Neurophysiology, Max-Planck-Institute for Medical Research, Jahnstr. 29, 69120 Heidelberg, Germany. schaefer@mpimf-heidelberg.mpg.de
Abstract:
Genetics' demand for increased throughput is driving automatization of behavior analysis far beyond experimental workhorses like circadian monitors and the operant conditioning box. However, the new automation is not just faster: it is also allowing new kinds of experiments, many of which erase the boundaries of the traditional neuroscience disciplines (psychology, ethology and physiology) while producing insight into problems that were otherwise opaque. Ironically, a central theme of current automatization is to improve observation of animals in increasingly naturalistic environments. This is not just a return to 19th century priorities: the new observational methods provide unprecedented quantitation of actions and ever-closer integration with experimentation.
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