Neural activity exhibits oscillations, bursts, and resonance, enhancing responsiveness at preferential frequencies. For example, theta-frequency bursting and resonance in granule cells facilitate synaptic transmission and plasticity mechanisms at the input stage of the cerebellar cortex. However, whether theta-frequency bursting of Purkinje cells is involved in generating rhythmic behavior has remained neglected. We recorded and optogenetically modulated the simple and complex spike activity of Purkinje cells while monitoring whisker movements with a high-speed camera of awake, head-fixed mice. During spontaneous whisking, both simple spike activity and whisker movement exhibit peaks within the theta band. Eliciting either simple or complex spikes at frequencies ranging from 0.5 to 28 Hz, we found that 8 Hz is the preferred frequency around which the largest movement is induced. Interestingly, oscillatory whisker movements at 8 Hz were also generated when simple spike bursting was induced at 2 and 4 Hz, but never via climbing fiber stimulation. These results indicate that 8 Hz is the resonant frequency at which the cerebellar-whisker circuitry produces rhythmic whisking.

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Rodents engage in active touch using their facial whiskers: they explore their environment by making rapid back-and-forth movements. The fast nature of whisker movements, during which whiskers often cross each other, makes it notoriously difficult to track individual whiskers of the intact whisker field. We present here a novel algorithm, WhiskEras, for tracking of whisker movements in high-speed videos of untrimmed mice, without requiring labeled data. WhiskEras consists of a pipeline of image-processing steps: first, the points that form the whisker centerlines are detected with sub-pixel accuracy. Then, these points are clustered in order to distinguish individual whiskers. Subsequently, the whiskers are parameterized so that a single whisker can be described by four parameters. The last step consists of tracking individual whiskers over time. We describe that WhiskEras performs better than other whisker-tracking algorithms on several metrics. On our four video segments, WhiskEras detected more whiskers per frame than the Biotact Whisker Tracking Tool. The signal-to-noise ratio of the output of WhiskEras was higher than that of Janelia Whisk. As a result, the correlation between reflexive whisker movements and cerebellar Purkinje cell activity appeared to be stronger than previously found using other tracking algorithms. We conclude that WhiskEras facilitates the study of sensorimotor integration by markedly improving the accuracy of whisker tracking in untrimmed mice.

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Automated whisker tracking is important for researching active touch in rodents. Earlier efforts to detect whiskers and represent them in a small set of parameters were either not accurate enough to enable tracking over time, or computationally expensive. In this article we propose an algorithm to cluster whisker centerline points, detected through a curvilinear structure algorithm, using the shape of smaller clusters to form bigger clusters of centerline points. After that, a least-squares approach is used to define each whisker by a set of four parameters. We implemented the algorithm in MATLAB in a parallelized fashion, and found that the processing time per frame is reasonable in MATLAB, and is likely to be short when ported to a lower-level language. When tested on a 33,634-frame segment, 89.2% of the whiskers could be represented in an abstract fashion by four parameters with a mean-squares fitting error of lower than 10 pixels, and visual inspection shows that crossing whiskers are detected and parameterized in an accurate way.

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Romano et al. show that cerebellar Purkinje cell activity follows the respiratory rhythm during rest. Triggered by sensory input, Purkinje cells can alter their activity and thereby accelerate the timing of the next inspiration. Concomitantly, they also augment whisker movements, highlighting a coordinating role in aligning autonomic and sensorimotor behaviors.

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The rodent whisker system is a prominent experimental subject for the study of sensorimotor integration and active sensing. As a result of improved video-recording technology and progressively better neurophysiological methods, there is now the prospect of precisely analyzing the intact vibrissal sensorimotor
system. The vibrissae and snout analyzer (ViSA), a widely used algorithm based on computer vision and image processing, has been proven successful for tracking and quantifying rodent sensorimotor behavior, but at a great cost in processing time. In order to accelerate this offline algorithm and eventually
employ it for online whisker tracking (less than 1 ms/frame latency), we have explored various optimizations and acceleration platforms, including OpenMP multithreading, NVidia GPUs and Maxeler Dataflow Engines. Our experimental results indicate that the optimal solution for an offline implementation of ViSA is
currently the OpenMP-based CPU execution. By using 16 CPU threads, we achieve more than 4,500x speedup compared to the original Matlab serial version, resulting in an average processing latency of 1.2 ms/frame, which is a solid step towards real-time (and online) tracking. Analysis shows that running the algorithm on a 32-thread-enabled machine can reduce this number to
0.72 ms/frame, thereby enabling real-time performance. This will allow direct interaction with the whisker system during behavioral experiments. In conclusion, our approach shows that a combination of software optimizations and the careful selection of hardware platform yields the best performance increase.@en