A popular hypothesis is that the dorsal striatum generates discrete "traffic-light" signals that initiate, maintain and terminate the execution of learned actions. Alternatively, the striatum may continuously monitor the dynamics of movements associated with action execution by processing inputs from somatosensory and motor cortices. Here we recorded the activity of striatal neurons in mice performing a run-and-stop task and characterized the diversity of firing rate modulations relative to run performance (tuning curves) across neurons. We found that the tuning curves could not be statistically clustered in discrete functional groups (start or stop neurons). Rather, their shape varied continuously according to the movement dynamics of the task. Moreover, striatal spiking activity correlated with running speed on a run-by-run basis, and was modulated by task-related non-locomotor movements such as licking. We hypothesize that such moment-to-moment movement monitoring by the dorsal striatum contributes to the learning of adaptive actions and/or updating their kinematics.

While miniature inertial sensors offer a promising means for precisely detecting, quantifying and classifying animal behaviors, versatile inertial sensing devices adapted for small, freely-moving laboratory animals are still lacking. We developed a standalone and cost-effective platform for performing high-rate wireless inertial measurements of head movements in rats. Our system is designed to enable real-time bidirectional communication between the headborne inertial sensing device and third party systems, which can be used for precise data timestamping and low-latency motion-triggered applications. We illustrate the usefulness of our system in diverse experimental situations. We show that our system can be used for precisely quantifying motor responses evoked by external stimuli, for characterizing head kinematics during normal behavior and for monitoring head posture under normal and pathological conditions obtained using unilateral vestibular lesions. We also introduce and validate a novel method for automatically quantifying behavioral freezing during Pavlovian fear conditioning experiments, which offers superior performance in terms of precision, temporal resolution and efficiency. Thus, this system precisely acquires movement information in freely-moving animals, and can enable objective and quantitative behavioral scoring methods in a wide variety of experimental situations.