Objective. The advent of high-performance computing (HPC) in recent years has led to its increasing use in brain studies through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. Approach. In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU, a NVidia GP-GPU and a Maxeler Dataflow Engine. The PyNN software framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different experiment instances of a state-of-the-art neuron model, representing the inferior-olivary nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal-network dimensions but also different network-connectivity densities, which can drastically affect the workload's performance characteristics. Main results. The combined use of different HPC technologies demonstrates that BrainFrame is better able to cope with the modeling diversity encountered in realistic experiments while at the same time running on significantly lower energy budgets. Our performance analysis clearly shows that the model directly affects performance and all three technologies are required to cope with all the model use cases. Significance. The BrainFrame framework is designed to transparently configure and select the appropriate back-end accelerator technology for use per simulation run. The PyNN integration provides a familiar bridge to the vast number of models already available. Additionally, it gives a clear roadmap for extending the platform support beyond the proof of concept, with improved usability and directly useful features to the computational-neuroscience community, paving the way for wider adoption.

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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.@en

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.@en

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.@en

Substrates for neuron culture and implantation are required to be both biocompatible and display surface compositions that support cell attachment, growth, differentiation, and neural activity. Laminin, a naturally occurring extracellular matrix protein is the most widely used substrate for neuron culture and fulfills some of these requirements, however, it is expensive, unstable (compared to synthetic materials), and prone to batch-to-batch variation. This study uses a high-throughput polymer screening approach to identify synthetic polymers that supports the in vitro culture of primary mouse cerebellar neurons. This allows the identification of materials that enable primary cell attachment with high viability even under “serum-free” conditions, with materials that support both primary cells and neural progenitor cell attachment with high levels of neuronal biomarker expression, while promoting progenitor cell maturation to neurons.@en

Miniaturized fluorescence microscopes (miniscopes) have been instrumental to monitor neural signals during unrestrained behavior and their open-source versions have made them affordable. Often, the footprint and weight of open-source miniscopes is sacrificed for added functionality. Here, we present NINscope: a light-weight miniscope with a small footprint that integrates a high-sensitivity image sensor, an inertial measurement unit and an LED driver for an external optogenetic probe. We use it to perform the first concurrent cellular resolution recordings from cerebellum and cerebral cortex in unrestrained mice, demonstrate its optogenetic stimulation capabilities to examine cerebello-cerebral or cortico-striatal connectivity, and replicate findings of action encoding in dorsal striatum. In combination with cross-platform acquisition and control software, our miniscope is a versatile addition to the expanding tool chest of open-source miniscopes that will increase access to multi-region circuit investigations during unrestrained behavior.

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Modern Implantable Medical Devices (IMDs) are vulnerable to security attacks because of their wireless connectivity to the outside world. One of the main security challenges is establishing trust between the IMD and an external reader/programmer in order to facilitate secure communication. Numerous device-pairing schemes have been proposed to address this specific challenge. However, they alone cannot protect against a battery-depletion attack in which the adversary is able to keep the IMD occupied with continuous authentication requests until the battery empties. As a result, energy harvesting has been employed as an ancillary mechanism for implementing Zero-Power Defense (ZPD) functionality in order to protect against such a low-cost attack. In this paper, we propose SecureEcho, a device-pairing scheme based on MHz-range ultrasound that establishes trust between the IMD and an external reader. In addition, SecureEcho achieves ZPD without requiring any energy harvesting, which significantly reduces the design complexity. We also provide a proof-of-concept implementation and a first ever security evaluation of the ultrasound channel, which proves that it is infeasible for the attacker to eavesdrop or insert messages even from a range of a few millimeters.

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Brain modeling has been receiving significant attention over the years, both for its neuroscientific potential and for its challenges in the context of high-performance computing. The development of physiologically plausible neuron models comes at the cost of increased complexity. In this work, we have selected a highly computationally demanding model of the Inferior-Olivary Nucleus (InfOli) based on the Hodgkin-Huxley (HH) neuron model. This brain region, functionally coupled with the cerebellum, is of vital importance for motor skills and time-sensitive cognitive functions. The computing fabric of choice is an Intel Xeon/Xeon Phi system, which is a typical node of modern computing infrastructure. The target application is parallelized with various combinations of MPI and OpenMP and performance is measured on the target platform. The different implementations are compared and the best one is chosen. Further optimization of this implementation is presented in detail. Its behaviour is then examined when scaling up to neuron populations representative of realistic, human Inferior-Olivary neuronal networks. The evaluation's results highlight the importance of examining a network's size and density before choosing the best platform for its simulation. All the parallelization and vectorization options presented in the current paper are available on a public repository for further examination.@en

The development of physiologically plausible neuron models comes with increased complexity, which poses a challenge for many-core computing. In this work, we have chosen an extension of the demanding Hodgkin-Huxley model for the neurons of the Inferior Olivary Nucleus, an area of vital im-portance for motor skills. The computing fabric of choice is an Intel Xeon-Xeon Phi system, widely-used in modern computing infrastructure. The target application is paral-lelized with combinations of MPI and OpenMP. The best configurations are scaled up to human InfOli numbers.@en

The development of physiologically plausible neuron models comes with increased complexity, which poses a challenge for many-core computing. In this work, we have chosen an extension of the demanding Hodgkin-Huxley model for the neurons of the Inferior Olivary Nucleus, an area of vital im-portance for motor skills. The computing fabric of choice is an Intel Xeon-Xeon Phi system, widely-used in modern computing infrastructure. The target application is paral-lelized with combinations of MPI and OpenMP. The best configurations are scaled up to human InfOli numbers.@en

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

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

This article presents a new family of scalable arithmetic units (ScAUs) targeting resource-constrained, embedded devices. We, first, study the performance, power, area and scalability properties of general adders. Next, suitable error-detection schemes for low-power embedded systems are discussed. As a result, our ScAUs are enhanced with a suitable error-detection scheme, resulting in a Parity-Checked ScAU (PCScAU) design. The PCScAU strikes a flexible trade-off between space and time redundancy, offering dependability similar to high-end techniques for the area and power cost of low-end approaches. An alternative design, the Precision-Scalable Arithmetic Unit (PScAU) maintains throughput with degraded precision in case of hardware failures. The PScAU is targeting dependable applications where latency rather than numerical accuracy is more important. The PScAU's downscaled mode is also interesting for runtime thermal management due to its advantageous power consumption. We implemented and synthesized the PCScAU, PScAU and a few important reference designs (double-, triple- and quadruple-modular-redundancy adders with/without input gating) in 90-nm UMC technology. Overall, the PC-ScAU ranks first in 9 out of 10 power-delay-area (PDA)-product variants. It exhibits 16% area savings and 12% performance speedup for 7% increase in total power consumption, compared to the cheapest form of conventional hardware replication with the same fault coverage. The PDA product of the PCScAU is, thus, reduced by 21%. It is interesting that, while total power slightly increases, the PCScAU static power in fact decreases by 14%. Therefore, for newer technology nodes where the static power component is significant, the PCScAU can also achieve—next to performance and area -- significant power improvements.@en

Synaptic and intrinsic processing in Purkinje cells, interneurons and granule cells of the cerebellar cortex have been shown to underlie various relatively simple, single-joint, reflex types of motor learning, including eyeblink conditioning and adaptation of the vestibulo-ocular reflex. However, to what extent these processes contribute to more complex, multi-joint motor behaviors, such as locomotion performance and adaptation during obstacle crossing, is not well understood. Here, we investigated these functions using the Erasmus Ladder in cell-specific mouse mutant lines that suffer from impaired Purkinje cell output (Pcd), Purkinje cell potentiation (L7-Pp2b), molecular layer interneuron output (L7-Δγ2), and granule cell output (α6-Cacna1a). We found that locomotion performance was severely impaired with small steps and long step times in Pcd and L7-Pp2b mice, whereas it was mildly altered in L7-Δγ2 and not significantly affected in α6-Cacna1a mice. Locomotion adaptation triggered by pairing obstacle appearances with preceding tones at fixed time intervals was impaired in all four mouse lines, in that they all showed inaccurate and inconsistent adaptive walking patterns. Furthermore, all mutants exhibited altered front–hind and left–right interlimb coordination during both performance and adaptation, and inconsistent walking stepping patterns while crossing obstacles. Instead, motivation and avoidance behavior were not compromised in any of the mutants during the Erasmus Ladder task. Our findings indicate that cell type-specific abnormalities in cerebellar microcircuitry can translate into pronounced impairments in locomotion performance and adaptation as well as interlimb coordination, highlighting the general role of the cerebellar cortex in spatiotemporal control of complex multi-joint movements.@en