The cloud has become a powerful and useful environment for the deployment of High-Performance Computing (HPC) applications, but the large number of available instance types poses a challenge in selecting the optimal platform. Users often do not have the time or knowledge necessary to make an optimal choice. Recommender systems have been developed for this purpose but current state-of-the-art systems either require large amounts of training data, or require running the application multiple times; this is costly. In this work, we propose Oikonomos-II, a resource-recommendation system based on reinforcement learning for HPC applications in the cloud. Oikonomos-II models the relationship between different input parameters, instance types, and execution times. The system does not require any preexisting training data or repeated job executions, as it gathers its own training data opportunistically using user-submitted jobs, employing a variant of the Neural-LinUCB algorithm. When deployed on a mix of HPC applications, Oikonomos-II quickly converged towards an optimal policy. The system eliminates the need for preexisting training data or auxiliary runs, providing an economical, general-purpose, resource-recommendation system for cloud HPC.@en

The cloud has become a powerful environment for deploying High-Performance Computing (HPC) applications. However, the size and heterogeneity of cloud-hardware offerings poses a challenge in selecting the optimal cloud instance type. Users often lack the knowledge or time necessary to make an optimal choice. In this work, we propose Oikonomos, a data-driven, opportunistic, resource-recommendation system for HPC applications in the cloud. Oikonomos trains a Multi-layer Perceptron (MLP) to predict the performance of a given HPC application, for different input parameters and instance types. It, then, calculates the cost of executing the application on different instance types and proposes the one best-fitting the user's needs. We deployed Oikonomos on a diverse mix of HPC workloads, and found that for all applications, it approached an optimal policy. The optimal instance type was chosen in 90% of the cases for seven out of eight applications, scoring a Mean Absolute Percentage Error (MAPE) consistently below 20%. This demonstrated that Oikonomos can provide a practical, general-purpose, resource-recommendation system for cloud HPC.

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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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Mice and rats can rapidly move their whiskers when exploring the environment. Accurate description of these movements is important for behavioral studies in neuroscience. Whisker tracking is, however, a notoriously difficult task due to the fast movements and frequent crossings and juxtapositionings among whiskers. We have recently developed WhiskEras, a computer-vision-based algorithm for whisker tracking in untrimmed, head-restrained mice. Although WhiskEras excels in tracking the movements of individual unmarked whiskers over time based on high-speed videos, the initial version of WhiskEras still had two issues preventing its widespread use: it involved tuning a great number of parameters manually to adjust for different experimental setups, and it was slow, processing less than 1 frame per second. To overcome these problems, we present here WhiskEras 2.0, in which the unwieldy stages of the initial algorithm were improved. The enhanced algorithm is more robust, not requiring intense parameter tuning. Furthermore, it was accelerated by first porting the code from MATLAB to C++ and then using advanced parallelization techniques with CUDA and OpenMP to achieve a speedup of at least 75x when processing a challenging whisker video. The improved WhiskEras 2.0 is made publicly available and is ready for processing high-speed videos, thus propelling behavioral research in neuroscience, in particular on sensorimotor integration.

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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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