The Effect of synchronized inputs at the single neuron level

It is commonly assumed that temporal synchronization of excitatory synaptic inputs onto a single neuron increases its firing rate. We investigate here the role of synaptic synchronization for the leaky integrate-and-fire neuron as well as for a biophysically and anatomically detailed compartmental model of a cortical pyramidal cell. We find that if the number of excitatory inputs, N, is on the same order as the number of fully synchronized inputs necessary to trigger a single action potential, N_t, synchronization always increases the firing rate (for both constant and Poisson-distributed input). However, for large values of N compared to N_t, ''overcrowding'' occurs and temporal synchronization is detrimental to firing frequency. This behavior is caused by the conflicting influence of the low-pass nature of the passive dendritic membrane on the one hand and the refractory period on the other. If both temporal synchronization as well as the fraction of synchronized inputs (Murthy and Fetz 1993) is varied, synchronization is only advantageous if either N or the average input frequency, ƒ(in), are small enough

Utilizing topology based domain segmentation for in-situ identification and classification of vortices in simulations of turbulent flows

Fully turbulent flow fields are populated with features such as vortices or eddies. Analysing flow features and their interaction can lead to insight into the physics of turbulence. An enhanced understanding of turbulence benefits, for example, the development of turbulence models, which can help dramatically reducing the computational costs of turbulent flow simulations. The vast abundance of features in fully turbulent flow fields demands an automated identification and analysis process. To address this issue Tracer was developed, an in-situ software framework to extract flow features from data produced by high-fidelity unsteady computational fluid dynamics (CFD) simulations conducted on GPU systems. Unsteady CFD simulations can produce a significant amount of data. The majority of this data cannot be accessed following the classical paradigm of storing the flow field to disk due to the bottleneck of writing the data disk and limitations of available storage capacity. To avoid the restrictions related to moving and storing data, Tracer is able to run concurrently with the simulation, analysing the data while it is still on the GPU's system memory. The intensities of flow features generally span multiple orders of magnitude making their identification a challenging task. Contrary to the classic approach of extracting features by defining a global threshold of a scalar $f$, Tracer identifies an individual threshold for each feature based on the topology of $f$, facilitating the identification of features over a wide range of scales and intensities. The individual thresholds are identified using a join tree, which tracks the topology of superlevel sets of $f$. A performance analysis showed that Tracer requires less system memory than the standalone CFD solver. Adding Tracer in-situ to a CFD simulation hence comes with a reasonable increase in system memory usage. The increase in runtime depends on the number of time steps that the CFD solver takes between two applications of Tracer to the flow field, but is typically within the single-digit percentage range. The advantages of topology based feature identification using Tracer versus the classic approach of using a global threshold are demonstrated qualitatively by visualising vortices in flow around an SD7003 aerofoil. The application of Tracer is demonstrated on the simulation of the turbulent transition of a Taylor-Green vortex with the aim of counting the number of vortices over time. If vortices are extracted based on the topolgy of the $Q$-criterion field a steep increase in the number of vortices can be observed during turbulent breakdown and a decrease during the decay of turbulence. Additionally a feature based analysis of turbulent channel flows up to $Re_\tau = 550$ was conducted examining the topology and the geometry of vortices. While the topological organisation of individual vortices in the near-wall and in the central region are found to be indistinguishable, there is a difference when vortex clusters are extracted. It was also found that vortices less than 70 wall units away from the wall tend to align in the streamwise direction; vortices further away from the wall were found to be geometrically isotropic. These results furthermore support the assumption that the diameter of elongated vortices scales with the Kolmogorov length.Open Acces

A Comprehensive Review on Ontologies for Scenario-based Testing in the Context of Autonomous Driving

The verification and validation of autonomous driving vehicles remains a major challenge due to the high complexity of autonomous driving functions. Scenario-based testing is a promising method for validating such a complex system. Ontologies can be utilized to produce test scenarios that are both meaningful and relevant. One crucial aspect of this process is selecting the appropriate method for describing the entities involved. The level of detail and specific entity classes required will vary depending on the system being tested. It is important to choose an ontology that properly reflects these needs. This paper summarizes key representative ontologies for scenario-based testing and related use cases in the field of autonomous driving. The considered ontologies are classified according to their level of detail for both static facts and dynamic aspects. Furthermore, the ontologies are evaluated based on the presence of important entity classes and the relations between them

Synchronization, oscillations, and 1/f noise in networks of spiking neurons

We investigate a model for neural activity that generates long range temporal correlations, 1/ f noise, and oscillations in global activity. The model consists of a two-dimensional sheet of leaky integrate-and- fire neurons with feedback connectivity consisting of local excitation and surround inhibition. Each neuron is independently driven by homogeneous external noise. Spontaneous symmetry breaking occurs, resulting in the formation of "hotspots" of activity in the network. These localized patterns of excitation appear as clusters that coalesce, disintegrate, or fluctuate in size while simultaneously moving in a random walk constrained by the interaction with other clusters. The emergent cross-correlation functions have a dual structure, with a sharp peak around zero on top of a much broader hill. The power spectrum associated with single units shows a 1/ f decay for small frequencies and is flat at higher frequencies, while the power spectrum of the spiking activity averaged over many cells-equivalent to the local field potential-shows no 1/ f decay but a prominent peak around 40 Hz

Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop

The process of developing control functions for embedded systems is resource-, time-, and data-intensive, often resulting in sub-optimal cost and solutions approaches. Reinforcement Learning (RL) has great potential for autonomously training agents to perform complex control tasks with minimal human intervention. Due to costly data generation and safety constraints, however, its application is mostly limited to purely simulated domains. To use RL effectively in embedded system function development, the generated agents must be able to handle real-world applications. In this context, this work focuses on accelerating the training process of RL agents by combining Transfer Learning (TL) and X-in-the-Loop (XiL) simulation. For the use case of transient exhaust gas re-circulation control for an internal combustion engine, use of a computationally cheap Model-in-the-Loop (MiL) simulation is made to select a suitable algorithm, fine-tune hyperparameters, and finally train candidate agents for the transfer. These pre-trained RL agents are then fine-tuned in a Hardware-in-the-Loop (HiL) system via TL. The transfer revealed the need for adjusting the reward parameters when advancing to real hardware. Further, the comparison between a purely HiL-trained and a transferred agent showed a reduction of training time by a factor of 5.9. The results emphasize the necessity to train RL agents with real hardware, and demonstrate that the maturity of the transferred policies affects both training time and performance, highlighting the strong synergies between TL and XiL simulation

Determination of nasal and oropharyngeal microbiomes in a multicenter population-based study – findings from Pretest 1 of the German National Cohort

We examined acceptability, preference and feasibility of collecting nasal and oropharyngeal swabs, followed by microbiome analysis, in a population-based study with 524 participants. Anterior nasal and oropharyngeal swabs were collected by certified personnel. In addition, participants self-collected nasal swabs at home four weeks later. Four swab types were compared regarding (1) participants’ satisfaction and acceptance and (2) detection of microbial community structures based on deep sequencing of the 16 S rRNA gene V1–V2 variable regions. All swabbing methods were highly accepted. Microbial community structure analysis revealed 846 phylotypes, 46 of which were unique to oropharynx and 164 unique to nares. The calcium alginate tipped swab was found unsuitable for microbiome determinations. Among the remaining three swab types, there were no differences in oropharyngeal microbiomes detected and only marginal differences in nasal microbiomes. Microbial community structures did not differ between staff-collected and self-collected nasal swabs. These results suggest (1) that nasal and oropharyngeal swabbing are highly feasible methods for human population-based studies that include the characterization of microbial community structures in these important ecological niches, and (2) that self-collection of nasal swabs at home can be used to reduce cost and resources needed, particularly when serial measurements are to be taken

Countrywide mapping of shrub forest using multi-sensor data and bias correction techniques

The continual increase of shrub forest in the Swiss Alps over the past few decades impacts biodiversity, forest succession and the protective function of forests. Therefore, up-to-date and area-wide information on its distribution is of great interest. To detect the shrub forest areas for the whole of Switzerland (41,285 km2), we developed an approach that uses Random Forest (RF), bias correction techniques and data from multiple remote sensing sources. Manual aerial orthoimage interpretation of shrub forest areas was conducted in a non-probabilistic way to derive initial training data. The multi-sensor and open access predictor data included digital terrain and vegetation height models obtained from Airborne Laser Scanning (ALS) and stereo-imagery, as well as Synthetic Aperture Radar (SAR) backscatter from Sentinel-1 and multispectral imagery from Sentinel-2. To mitigate the expected bias due to the training data sampling strategy, two techniques using RF probability estimates were tested to improve mapping accuracy. 1) an iterative and semi-automated active learning technique was used to generate further training data and 2) threshold-moving related object growing was applied. Both techniques facilitated the production of a shrub forest map for the whole of Switzerland at a spatial resolution of 10 m. An accuracy assessment was performed using independent data covering 7640 regularly distributed National Forest Inventory (NFI) plots. We observed the influence of the bias correction techniques and found higher accuracies after each performed iteration. The Mean Absolute Error (MAE) for the predicted shrub forest proportion was reduced from 6.04% to 2.68% while achieving a Mean Bias Error (MBE) of close to 0. The present study underscores the potential of combining multi-sensor data with bias correction techniques to provide cost-effective and accurate countrywide detection of shrub forest. Moreover, the map complements currently available NFI plot sample point data

Network Amplification of Local Fluctuations Causes High Spike Rate Variability, Fractal Firing Patterns and Oscillatory Local Field Potentials

We investigate a model for neural activity in a two-dimensional sheet of leaky integrate-and-fire neurons with feedback connectivity consisting of local excitation and surround inhibition. Each neuron receives stochastic input from an external source, independent in space and time. As recently suggested by Softky and Koch (1992, 1993), independent stochastic input alone cannot explain the high interspike interval variability exhibited by cortical neurons in behaving monkeys. We show that high variability can be obtained due to the amplification of correlated fluctuations in a recurrent network. Furthermore, the cross-correlation functions have a dual structure, with a sharp peak on top of a much broader hill. This is due to the inhibitory and excitatory feedback connections, which cause "hotspots" of neural activity to form within the network. These localized patterns of excitation appear as clusters or stripes that coalesce, disintegrate, or fluctuate in size while simultaneously moving in a random walk constrained by the interaction with other clusters. The synaptic current impinging upon a single neuron shows large fluctuations at many time scales, leading to a large coefficient of variation (C_V) for the interspike interval statistics. The power spectrum associated with single units shows a 1/f decay for small frequencies and is flat at higher frequencies, while the power spectrum of the spiking activity averaged over many cells—equivalent to the local field potential—shows no 1/f decay but a prominent peak around 40 Hz, in agreement with data recorded from cat and monkey cortex (Gray et al. 1990; Eckhorn et al. 1993). Firing rates exhibit self-similarity between 20 and 800 msec, resulting in 1/f-like noise, consistent with the fractal nature of neural spike trains (Teich 1992)

Interleukin-11 (IL-11) receptor cleavage by the rhomboid protease RHBDL2 induces IL-11 trans-signaling

Interleukin-11 (IL-11) is a pleiotropic cytokine with both pro- and anti-inflammatory properties. It activates its target cells via binding to the membrane-bound IL-11 receptor (IL-11R), which then recruits a homodimer of the ubiquitously expressed, signal-transducing receptor gp130. Besides this classic signaling pathway, IL-11 can also bind to soluble forms of the IL-11R (sIL-11R), and IL-11/sIL-11R complexes activate cells via the induction of gp130 homodimerization (trans-signaling). We have previously reported that the metalloprotease ADAM10 cleaves the membrane-bound IL-11R and thereby generates sIL-11R. In this study, we identify the rhomboid intramembrane protease RHBDL2 as a so far unrecognized alternative sheddase that can efficiently trigger IL-11R secretion. We determine the cleavage site used by RHBDL2, which is located in the extracellular part of the receptor in close proximity to the plasma membrane, between Ala-370 and Ser-371. Furthermore, we identify critical amino acid residues within the transmembrane helix that are required for IL-11R proteolysis. We also show that ectopically expressed RHBDL2 is able to cleave the IL-11R within the early secretory pathway and not only at the plasma membrane, indicating that its subcellular localization plays a central role in controlling its activity. Moreover, RHBDL2-derived sIL-11R is biologically active and able to perform IL-11 trans-signaling. Finally, we show that the human mutation IL-11R-A370V does not impede IL-11 classic signaling, but prevents RHBDL2-mediated IL-11R cleavage

Transition From Preliminary to Detailed Design of a Highly Elastic Solar Electric Aircraft

This work presents the evolution of the aeroelastic modeling of a solar electric aircraft. The dimensions (wing span of ≈27.0 m) and the extreme light-weight construction (wing loading ≈4.0 kg/m²) results in a highly flexible aircraft, making aeroelastic analyses mandatory in all stages of the aircraft design. Comparing the preliminary with the detailed design phase, the aeroelastic models are significantly improved in their level of detail. Still, the models show similar structural dynamic characteristics and a comparable loading, e.g. in terms of wing bending and torsional moments and , where the highest loads are caused by similar load cases. For the sizing, the analytic approach from the preliminary design is backed-up with numerical analyses using a detailed FE model. A comprehensive flutter analysis confirms the findings obtained from a flutter check during the preliminary design. Finally, first structural tests are prepared to validate the models