Behavioral raw data recorded by HABITS autonomously

Mice are among the most prevalent animal models in neuroscience, leveraging the extensive physiological, imaging and genetic tools available for studying their brain. However, the development of new behavioral paradigms for mice has been laborious and inconsistent, impeding the investigation of complex cognition. Here, we present a home-cage assisted mouse behavioral innovation and testing system (HABITS), enables free-moving mice learn challenging cognitive behaviors in their home-cage without any human involvement. Supported by the microcontroller-based general programming framework, we have not only replicated established paradigms in current neuroscience research but also developed several novel paradigms previously unexplored in mice, resulting in more than 300 mice tested in various cognition functions. Through a machine-teaching approach, HABITS can comprehensively optimize the presentation of stimuli and modalities for trials, leading to more efficient training and higher-quality behavioral outcomes. To our knowledge, this is the first instance where mouse behavior has been systematically optimized by an algorithmic approach. Our results open a new approach for mouse behavior and demonstrate the novel and optimized cognitions to be investigated at the neural level.