Novel and optimized mouse behavior enabled by fully autonomous HABITS: Home-cage assisted behavioral innovation and...

Area of Science:

• Neuroscience
• Animal Behavior
• Cognitive Science

Background:

• Mice are crucial animal models in neuroscience due to available genetic and imaging tools.
• Developing and optimizing behavioral paradigms for mice has been challenging and inconsistent.
• This inconsistency hinders the study of complex cognitive functions in mice.

Purpose of the Study:

• To introduce a novel automated system for behavioral innovation and testing in mice.
• To enable mice to learn challenging cognitive behaviors within their home cage without human intervention.
• To optimize behavioral training using a machine-teaching algorithm.

Main Methods:

• Development of the home-cage assisted mouse behavioral innovation and testing system (HABITS).
• Utilizing a general programming framework to support established and novel behavioral paradigms.
• Implementation of a machine-teaching algorithm for optimizing stimulus presentation and trial modalities.

Main Results:

• HABITS successfully replicated established paradigms and developed novel ones for mice.
• Over 300 mice were tested, demonstrating various cognitive functions.
• The machine-teaching algorithm significantly optimized training efficiency and behavioral outcome quality.

Conclusions:

• HABITS represents a significant advancement in mouse behavioral innovation and optimization.
• This system facilitates the investigation of neural circuits underlying novel cognitive functions in mice.
• It is the first documented instance of systematic algorithmic optimization for mouse behavior.