Hippocampus and striatum encode distinct task regularities that guide human timing behavior

The brain encodes the statistical regularities of the environment in a task-specific yet flexible and generalizable format. How it does so remains poorly understood. Here, we seek to understand this by converging two parallel lines of research, one centered on striatal-dependent sensorimotor timing, and the other on hippocampal-dependent cognitive mapping. We combined functional magnetic resonance imaging (fMRI) with a visual-tracking and time-to-contact (TTC) estimation task, revealing the widespread brain network supporting sensorimotor learning in real-time. Hippocampal and caudate activity signaled the behavioral feedback within trials and the improvements in performance across trials, suggesting that both structures encode behavior-dependent information rapidly. Critically, hippocampal learning signals generalized across tested intervals, while striatal ones did not, and together they explained both the trial-wise performance and the regression-to-the-mean biases in TTC estimation. Our results suggest that a fundamental function of hippocampal-striatal interactions may be to solve a trade-off between specificity vs. generalization, enabling the flexible and domain-general expression of human timing behavior broadly.

Investigation of Timing Behavior and Jitter in a Smart Inertial Sensor Debugging Architecture

Due to upcoming higher integration levels of microprocessors, the market of inertial sensors has changed in the last few years. Smart inertial sensors are becoming more and more important. This type of sensor offers the benefit of implementing sensor-processing tasks directly on the sensor hardware. The software development on such sensors is quite challenging. In this article, we propose an approach for using prerecorded sensor data during the development process to test and evaluate the functionality and timing of the sensor firmware in a repeatable and reproducible way on the actual hardware. Our proposed Sensor-in-the-Loop architecture enables the developer to inject sensor data during the debugging process directly into the sensor hardware in real time. As the timing becomes more critical in future smart sensor applications, we investigate the timing behavior of our approach with respect to timing and jitter. The implemented approach can inject data of three 3-DOF sensors at 1.6 kHz. Furthermore, the jitter shown in our proposed sampling method is at least three times lower than using real sensor data. To prove the statistical significance of our experiments, we use a Gage R&R analysis, extended by the assessment of confidence intervals of our data.