Multimodal data acquisition at SARS-CoV-2 drive through screening centers: Setup description and experiences in Saarland, Germany

SARS-CoV-2 drive through screening centers (DTSC) have been implemented worldwide as a fast and secure way of mass screening. We use DTSCs as a platform for the acquisition of multimodal datasets that are needed for the development of remote screening methods. Our acquisition setup consists of an array of thermal, infrared and RGB cameras as well as microphones and we apply methods from computer vision and computer audition for the contactless estimation of physiological parameters. We have recorded a multimodal dataset of DTSC participants in Germany for the development of remote screening methods and symptom identification. Acquisition in the early stages of a pandemic and in regions with high infection rates can facilitate and speed up the identification of infection specific symptoms and large-scale data acquisition at DTSC is possible without disturbing the flow of operation

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Experimentelle und numerische Untersuchungen zur Formgebung von Tailored Strips und Tailored Tubes

Through the application of tailored strips and tailored tubes, the wall thickness of components can be manufactured in a load-optimised manner. Thus, it is also possible to optimise component weight. Prior to the application of tailored products, wall thicknesses and the respective degree of deformation as well as the welding seam position can be determined in a FEM (finite element method) simulation. These results are then verified in test series on transfer presses and tube bending machines, with the necessary tool adaptations being determined in the process. This results in weight and cost reductions for deep-drawn components and tube sections. Moreover, this means that especially with regard to tubes, multiple sections can be combined in one component. A feasibility study shows that the level of possible weight and cost savings depends on the respective component geometry and load situation. Additional costs for the production of tailored products and - if necessary - tool modifications also need to be considered. Thus, the amount of savings possible for a part can only be determined on an individual basis

Correlated activity of periodically driven binary networks

Experiments showed that excess synchronous spike events are locked to the phase of LFP beta-oscillations more strongly than spikes not part of such events [Denker et al. 2011, Cereb. Cortex]. To identify themechanisms by which correlations depend on the phase of the LFP,which primarily reflects input activity, we examine a balanced network of homogeneously connected binary model neurons [Ginzburg etal. 1994, PRE] receiving input from a sinusoidal perturbation. The Glauber dynamics of the network is simulated and approximated by mean-field theory. Treating the periodic input in linear response theory,the cyclostationary first two moments are analytically computed. They agree with their simulated counterparts over a wide parameter range. The zero-time lag correlations consist of two terms, one due to the modulated susceptibility (via the external input and network feedback) and one due to the time-varying autocorrelations. For some parameters, this leads to resonant correlations and non-resonant mean activities. Our results can help to answer the salient question how oscillations in mesoscopic signals and spike correlations interact. Supported by the Helmholtz foundation (VH-NG-1028, SMHB); EUGrant 604102 (HBP). Simulations with NEST (nest-simulator.org)

How does an oscillatory drive shape the correlations in binary networks?

Two important parts of electrophysiological recordings are the spike times and the local field potential (LFP), which is considered to primarily reflect input activity. In [1], it was shown by unitary event analysis [2,3] that excess synchronous spike events are locked to the phase of LFP beta-oscillations more strongly than spikes not part of such events. Denker et al. proved by a statistical model that this finding could be explained by the existence of cell assemblies, i.e. groups of (excitatory) neurons that are more strongly connected amongst each other than to the rest of the network.To study the influence of the LFP on the correlated single neuron activities first for a simple model capturing the main properties of cortical neural networks, we examine a balanced network of homogeneously connected binary model neurons [4] receiving input from a sinusoidal perturbation [5]. The Glauber dynamics of the network is simulated and approximated by mean-field theory. Treating the periodic input in linear response theory, the cyclostationary first two moments are analytically computed, which agree with their simulated counterparts over a wide parameter range. The deviations of the zero-time lag correlations from their stationary values consist of two summands owing to the modulated susceptibility (one via direct modulation, one via modulated mean activity) and one to the driving of the autocorrelations. For some parameters, this leads to resonant correlations and non-resonant mean activities. Our results can help to answer the question how oscillations in mesoscopic signals and spike correlations interact. As a next step, our model could be extended to include cell assemblies [6], which will allow us to compare our results with the experimental findings more closely.figure caption:A: Contributions to the time-dependent variation of the correlations in linear perturbation theory. B: The deviation of the correlations from their stationary value is maximal for a certain frequency even for this setting with a connectivity matrix having solely purely real eigenvalues. References:[1] Denker M., Cerebral Cortex, 21:2681--2695, 2011, The Local Field Potential Reflects Surplus Spike Synchrony. [2] Grün S, Diesmann M, Aertsen A. Neural Comput., 14:43--80, 2002a, Unitary events in multiple single-neuron spiking activity: I. detection and significance. [3] Grün S, Diesmann M, Aertsen A. Neural Comput., 14:81--119, 2002b, Unitary events in multiple single-neuron spiking activity: II. Nonstationary data. [4] Ginzburg I., Sompolinsky H., Phys. Rev. E 50(4):3171--3191, 1994, Theory of correlations in stochastic neural networks.[5] Kühn T., Helias M., arXiv:1607.08552, 2016, Correlated activity of periodically driven binary networks.[6] Litwin-Kumar A., & Doiron B., Nature Neur., 15(11):1498--1505, 2012, Slow dynamics and high variability in balanced cortical networks with clustered connections