Open-source platform for adjustable training regimes in freely moving and head-fixed mice

Abstract

Molecular tools available for rodent research enable detailed interrogation of the neural cell types and circuits that give rise to perception and decision-making during complex behaviors. To take full advantage of these molecular tools and successfully define causal relationships between neural function and overt actions during learning, there is a need for low-cost behavioral platforms with inherent flexibility in the implementation of task details. We present a behavioral platform capable of executing both head-fixed and freely moving task designs. The platform incorporates a user-interactive GUI that allows parameters to be adjusted on-line, during an acquisition session. Task metrics and performance indicators are acquired and organized into a standardized output, enabling single users to quickly master data analysis across a variety of task designs. To demonstrate the flexibility of the platform, mice of either sex were trained in two discrimination tasks: a head-fixed two-choice task as well as a freely moving operant conditioning task. Furthermore, we demonstrate that the platform can be used to show that mice harboring a mutation associated with autism spectrum disorder are able to perform a basic visual discrimination task in freely moving conditions. The presented work demonstrates the integration of multiple external devices to record task-related variables in a synchronized manner. As a result, the platform provides a valuable tool for affordable and reproducible investigation of behavioral decision making as well as the neural basis underlying cognitive processes in health and disease.

Significance Statement An open-source, low-cost solution to implementing complex rodent behavioral training paradigms in head-fixed and freely moving conditions is presented. The platform offers flexibility in that it can be coupled to any number of external devices, each with unique sampling rates, task structures are composed of modular epochs, and on-the-fly adjustments to parameters critical for optimizing training can be made. Acquired data are organized into a standardized format, facilitating data visualization and analysis following updates to external devices, troubleshooting, and across a wide variety of behavioral task designs, including closed-loop systems.