Oikonomos-II+ is a hybrid, reinforcement-learning system for recommending optimal cloud-instance types for High-Performance Computing (HPC) and Artificial-Intelligence (AI) applications. Unlike existing approaches that require historical data or repeated job executions, Oikonomos-II+ learns online using user-submitted jobs. It combines a modified Neural-LinUCB algorithm with Gaussian-Process regression to model the relationship between job parameters, instance types, and execution time. This allows it to balance exploration and exploitation efficiently, even in the absence of prior data. We evaluated six configurations of Oikonomos-II+ on a diverse set of HPC and AI workloads, optimizing for cost and speed. Results show that the complete system converges to optimal resource choices, outperforming purely predictive or search-based approaches. By treating deployed applications as a black box and by eliminating the need for preexisting training data or auxiliary runs, Oikonomos-II+ provides a general-purpose, low-overhead solution for dynamic resource selection in heterogeneous cloud environments.

Olivocerebellar learning is highly adaptable, unfolding over minutes to weeks depending on the task. However, the stabilizing mechanisms of the synaptic dynamics necessary for ongoing learning remain unclear. We constructed a model to examine plasticity dynamics under stochastic input and investigate the impact of inferior olive (IO) reverberations on Purkinje cell (PCs) activity and synaptic plasticity. We explored Upbound and Downbound cerebellar micromodules, which are organized loops of IO neurons, cerebellar nuclei neurons and microzones of PCs characterized by their unique molecular profiles and different levels of baseline firing. Our findings show synaptic weight convergence followed by stability of synaptic weights. In line with their relatively low and high intrinsic firing, we observed that Upbound and Downbound PCs have a propensity for potentiation and depression, respectively, with both PC types reaching stability at differential levels of overall strength of their parallel-fiber (PF) inputs. The oscillations and coupling of IO neurons participating in the Upbound and Downbound modules determine at which frequency band PFs can be stabilized optimally. Our results indicate that specific frequency components drive IO resonance and synchronicity, which, in turn, regulate temporal patterning across Upbound and Downbound zones, orchestrating their plasticity dynamics.

According to the motor learning theory by Albus and Ito, synaptic depression at the parallel fibre to Purkinje cells synapse (pf-PC) is the main substrate responsible for learning sensorimotor contingencies under climbing fibre control. However, recent experimental evidence challenges this relatively monopolistic view of cerebellar learning. Bidirectional plasticity appears crucial for learning, in which different microzones can undergo opposite changes of synaptic strength (e.g. downbound microzones-more likely depression, upbound microzones-more likely potentiation), and multiple forms of plasticity have been identified, distributed over different cerebellar circuit synapses. Here, we have simulated classical eyeblink conditioning (CEBC) using an advanced spiking cerebellar model embedding downbound and upbound modules that are subject to multiple plasticity rules. Simulations indicate that synaptic plasticity regulates the cascade of precise spiking patterns spreading throughout the cerebellar cortex and cerebellar nuclei. CEBC was supported by plasticity at the pf-PC synapses as well as at the synapses of the molecular layer interneurons (MLIs), but only the combined switch-off of both sites of plasticity compromised learning significantly. By differentially engaging climbing fibre information and related forms of synaptic plasticity, both microzones contributed to generate a well-timed conditioned response, but it was the downbound module that played the major role in this process. The outcomes of our simulations closely align with the behavioural and electrophysiological phenotypes of mutant mice suffering from cell-specific mutations that affect processing of their PC and/or MLI synapses. Our data highlight that a synergy of bidirectional plasticity rules distributed across the cerebellum can facilitate finetuning of adaptive associative behaviours at a high spatiotemporal resolution.

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.

Inferior olivary activity causes both short-term and long-term changes in cerebellar output underlying motor performance and motor learning. Many of its neurons engage in coherent subthreshold oscillations and are extensively coupled via gap junctions. Studies in reduced preparations suggest that these properties promote rhythmic, synchronized output. However, the interaction of these properties with torrential synaptic inputs in awake behaving animals is not well understood. Here we combine electrophysiological recordings in awake mice with a realistic tissue-scale computational model of the inferior olive to study the relative impact of intrinsic and extrinsic mechanisms governing its activity. Our data and model suggest that if subthreshold oscillations are present in the awake state, the period of these oscillations will be transient and variable. Accordingly, by using different temporal patterns of sensory stimulation, we found that complex spike rhythmicity was readily evoked but limited to short intervals of no more than a few hundred milliseconds and that the periodicity of this rhythmic activity was not fixed but dynamically related to the synaptic input to the inferior olive as well as to motor output. In contrast, in the long-term, the average olivary spiking activity was not affected by the strength and duration of the sensory stimulation, while the level of gap junctional coupling determined the stiffness of the rhythmic activity in the olivary network during its dynamic response to sensory modulation. Thus, interactions between intrinsic properties and extrinsic inputs can explain the variations of spiking activity of olivary neurons, providing a temporal framework for the creation of both the short-term and long-term changes in cerebellar output.

In heart-beat-based security, a security key is derived from the time difference between consecutive heart beats (the inter-pulse interval, IPI), which may, subsequently, be used to enable secure communication. While heart-beat-based security holds promise in mobile health (mHealth) applications, there currently exists no work that provides a detailed characterization of the delivered security in a real system. In this paper, we evaluate the strength of IPI-based security keys in the context of entity authentication. We investigate several aspects that should be considered in practice, including subjects with reduced heart-rate variability (HRV), different sensor-sampling frequencies, intersensor variability (i.e., how accurate each entity may measure heart beats) as well as average and worst-case-authentication time. Contrary to the current state of the art, our evaluation demonstrates that authentication using multiple, less-entropic keys may actually increase the key strength by reducing the effects of intersensor variability. Moreover, we find that the maximal key strength of a 60-bit key varies between 29.2 bits and only 5.7 bits, depending on the subject's HRV. To improve security, we introduce the inter-multi-pulse interval (ImPI), a novel method of extracting entropy from the heart by considering the time difference between nonconsecutive heart beats. Given the same authentication time, using the ImPI for key generation increases key strength by up to 3.4 × (+19.2 bits) for subjects with limited HRV, at the cost of an extended key-generation time of 4.8 × (+45 s).

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.

In-vivo and in-vitro experiments are routinely used in neuroscience to unravel brain functionality. Although they are a powerful experimentation tool, they are also time-consuming and, often, restrictive. Computational neuroscience attempts to solve this by using biologically-plausible and biophysically-meaningful neuron models, most prominent among which are the conductance-based models. Their computational complexity calls for accelerator-based computing to mount large-scale or real-time neuroscientific experiments. In this paper, we analyze and draw conclusions on the class of conductance models by using a representative modeling application of the inferior olive (InfOli), an important part of the olivocerebellar brain circuit. We conduct an extensive profiling session to identify the computational and data-transfer requirements of the application under various realistic use cases. The application is, then, ported onto two acceleration nodes, an Intel Xeon Phi and a Maxeler Vectis Data Flow Engine (DFE). We evaluate the performance scalability and resource requirements of the InfOli application on the two target platforms. The analysis of InfOli, which is a real-life neuroscientific application, can serve as a useful guide for porting a wide range of similar workloads on platforms like the Xeon Phi or the Maxeler DFEs. As accelerators are increasingly populating High-Performance Computing (HPC) infrastructure, the current paper provides useful insight on how to optimally use such nodes to run complex and relevant neuron modeling workloads.

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.

The inferior olivary nucleus provides one of the two main inputs to the cerebellum: the so-called climbing fibers. Activation of climbing fibers is generally believed to be related to timing of motor commands and/or motor learning. Climbing fiber spikes lead to large all-or-none action potentials in cerebellar Purkinje cells, overriding any other ongoing activity and silencing these cells for a brief period of time afterwards. Empirical evidence shows that the climbing fiber can transmit a short burst of spikes as a result of an olivary cell somatic spike, potentially increasing the information being transferred to the cerebellum per climbing fiber activation. Previously reported results from in vitro studies suggested that the information encoded in the climbing fiber burst is related to the occurrence of the spike relative to the ongoing sub-threshold membrane potential oscillation of the olivary cell, i.e. that the phase of the oscillation is reflected in the size of the climbing fiber burst. We used a detailed three-compartmental model of an inferior olivary cell to further investigate the possible factors determining the size of the climbing fiber burst. Our findings suggest that the phase-dependency of the burst size is present but limited and that charge flow between soma and dendrite is a major determinant of the climbing fiber burst. From our findings it follows that phenomena such as cell ensemble synchrony can have a big effect on the climbing fiber burst size through dendrodendritic gap-junctional coupling between olivary cells.

This article explores dynamical properties of the olivo-cerebellar system that arise from the specific wiring of inferior olive (IO), cerebellar cortex, and deep cerebellar nuclei (DCN). We show that the irregularity observed in the firing pattern of the IO neurons is not necessarily produced by noise but can instead be the result of a purely deterministic network effect. We propose that this effect can serve as a dynamical working memory or as a neuronal clock with a characteristic timescale of about 100 ms that is determined by the slow calcium dynamics of IO and DCN neurons. This concept provides a novel explanation of how the cerebellum can solve timing tasks on a timescale that is two orders of magnitude longer than the millisecond timescale usually attributed to neuronal dynamics. One of the key ingredients of our model is the observation that due to postinhibitory rebound, DCN neurons can be driven by GABAergic ("inhibitory") input from cerebellar Purkinje cells. Topographic projections from the DCN to the IO form a closed reverberating loop with an overall synaptic transmission delay of about 100 ms that is in resonance with the intrinsic oscillatory properties of the inferior olive. We use a simple time-discrete model based on McCulloch-Pitts neurons in order to investigate in a first step some of the fundamental properties of a network with delayed reverberating projections. The macroscopic behavior is analyzed by means of a mean-field approximation. Numerical simulations, however, show that the microscopic dynamics has a surprisingly rich structure that does not show up in a mean-field description. We have thus performed extensive numerical experiments in order to quantify the ability of the network to serve as a dynamical working memory and its vulnerability by noise. In a second step, we develop a more realistic conductance-based network model of the inferior olive consisting of about 20 multicompartment neurons that are coupled by gap junctions and receive excitatory and inhibitory synaptic input via AMPA and GABAergic synapses. The simulations show that results for the time-discrete model hold true in a time-continuous description.