To make full use of optogenetic and molecular techniques in the study of motor control, rich behavioral paradigms for rodents must rise to the same level of sophistication and applicability. We describe the layout, construction, use and analysis of data from joystick-based reaching in a head-fixed mouse. The step-by-step guide is designed for both experienced rodent motor labs and new groups looking to enter into this research space. Using this platform, mice learn to consistently perform large, easily-quantified reaches, including during a two-armed bandit probabilistic learning task. The metrics of performance (reach trajectory, amplitude, speed, duration, and inter-reach interval) can be used to quantify behavior or administer stimulation in closed loop with behavior. We provide a highly customizable, low cost and reproducible open-source behavior Significance Statement We are realizing that the behavioral repertoire of mice is much richer than previously thought, including motor control and decision-making using reaches. Modern neuroscience is now capturing this richness, paired with new genetic tools, to understand fundamental neuroscience principles. Here, we provide an illustrated build guide, code, multiple use scenarios, and analytic tools to a low-cost, highly customizable mouse joystick. This tool will enable improved throughput, accessibility, and experimental design (e.g., spatiotemporal reach trajectories over lever presses) for labs wishing to study a range of reach-based experiments.

For decades, advanced behavioral tasks have only been used in human and non-human primates. However, with improved analytical and genetic techniques, there has been a growing drive to implement complex reaching, decision-making, and reaction time tasksnot in primates -but in rodents. Here, we assess the hypothesis that a mouse can learn a cued reaction time task. Moreover, we tested multiple training regimens and found that introducing elements of the reaction time task serially hindered, rather than helped task acquisition. Additionally, we include a step-by-step manual for inexpensive implementation and use of a rodent joystick for behavioral analysis. Task and analysis code for the evaluated behaviors are included such that they may be replicated and tested further. With these, we also include code for a probabilistic reward 'two-arm bandit' task. These various tasks, and the method to construct and implement them, will enable greatly improved study of the neural correlates of behavior in the powerful mouse model organism. In summary, we have tested and demonstrated that mice can learn sophisticated tasks with A joystick, and that targeted task design provides a significant advantage. These results of this study stand to inform the implementation of other sophisticated tasks using the mouse model.