Dendritic integration in olfactory bulb granule cells: Thresholds for lateral inhibition and role of active conductances upon 3D multi-site photostimulation of spines using a holographic projector module

The inhibitory axonless olfactory bulb granule cells (OB GCs) form reciprocal dendrodendritic synapses with mitral and tufted cells (MCs and TCs) via large spines, mediating recurrent and lateral inhibition. Rat GC dendrites are excitable by local Na⁺ spine spikes and global Ca²⁺- and Na⁺-spikes. Since reaching global threshold potentials also represents the onset of lateral inhibition, the goal of my work was to investigate the exact transition from local to global signalling: How many spines, in which position and distribution on the dendritic tree have to be activated to trigger global spikes and what are the molecular key players, i.e. which ion channels are involved. In the first part of this study we have integrated a holographic projector into the existing commercial two-photon (2P) Galvanometer-based 2D laser scanning microscope with an uncaging unit (Uncaging: Activation of photolabile biologically inactive derivatives of neurotransmitters by photolysis), which allows the simultaneous photostimulation of several spines in three dimensions (3D) in acute brain slices. Patterned 2P photolysis via holographic illumination is a powerful method to investigate neuronal function because of its capability to emulate multiple synaptic inputs in three dimensions (3D) simultaneously. However, like any optical system, holographic projectors have a finite space-bandwidth product that restricts the spatial range of patterned illumination or field-of-view (FOV) for a desired resolution. Such trade-off between holographic FOV and resolution restricts the coverage within a limited domain of the neuron’s dendritic tree to perform highly resolved patterned 2P photolysis on individual spines. Here, we integrate a holographic projector into a commercial 2P galvanometer-based 2D scanning microscope with an uncaging unit and extend the accessible holographic FOV by using the galvanometer scanning mirrors to reposition the holographic FOV arbitrarily across the imaging FOV. The projector system utilizes the microscope’s built-in imaging functions. Stimulation positions can be selected from within an acquired 3D image stack (the volume of interest, VOI) and the holographic projector then generates 3D illumination patterns with multiple uncaging foci. The imaging FOV of our system is 800×800 μm² within which a holographic VOI of 70×70×70 μm³ can be chosen at arbitrary positions and also moved during experiments without moving the sample. We describe the design and alignment protocol as well as the custom software plugin that controls the 3D positioning of stimulation sites. We demonstrate the neurobiological application of the system by simultaneously uncaging glutamate at multiple spines within dendritic domains and consequently observing summation of postsynaptic potentials at the soma, eventually resulting in APs. At the same time, it is possible to perform 2P Ca²⁺ imaging in 2D in the dendrite and thus to monitor synaptic Ca²⁺ entry in selected spines and also local regenerative events such as dendritic APs. In the second part of this study we applied the system to study dendritic integration in GCs. Less than 10 coactive reciprocal spines were sufficient to generate diverse regional and global signals that also included local dendritic Ca²⁺- and Na⁺-spikes (D-spikes). Individual spines could sense the respective signal transitions as increments in Ca²⁺ entry. Dendritic integration was mostly linear until a few spines below global Na⁺-spike threshold, where often D-spikes set in. NMDARs strongly contributed to active integration, whereas morphological parameters barely mattered. In summary, thresholds for GC-mediated bulbar lateral inhibition are low

Quantifying Linguistic Variation:Data-driven Navigation of Variety Space

Language emerges naturally from human communication, and as such, linguistic variation across the many possible dimensions of expression is ubiquitous. Higher variation across specific dimensions leads to a decrease in mutual intelligibility, or, in the case of Natural Language Processing (NLP), to decreased model transferability. Linguistics delineates between dimensions such as typology, domain, register, etc., using qualitative definitions, however, these are difficult to apply quantitatively and to combine at scale. NLP on the other hand necessitates a quantization of language, and has thus enabled machines to learn data-driven, vectorized representations thereof, which measure language similarity remarkably well, but fall short of explaining exactly how two data points are related. By leveraging probing methods to segment the high-dimensional latent spaces of Language Models (LMs) into subspaces with linguistically interpretable similarity characteristics, we aim to bridge the divide between these two disciplines. Our results for cross-lingual syntax and cross-domain genre demonstrate that corresponding subspaces can be successfully recovered, and consequently used to predict which training data and models transfer well to unseen language varieties and domains. Combining dimensions from across this Variety Space, we further quantify task similarity in an interpretable way, and investigate how linguistic information emerges in LMs during their training. As NLP increasingly relies on general purpose information stored in LMs to solve myriads of downstream tasks, we argue that quantifying and understanding language and task variation is critical to ensure model robustness and trustworthiness. Towards this goal, our quantitative measures of linguistic variation provide a generally applicable framework grounded in traditional linguistics

Discontinuous Galerkin Finite Element Convergence for Incompressible Miscible Displacement Problems of Low Regularity

In this article we analyse the numerical approximation of incompressible miscible displacement problems with a combined mixed finite element and discontinuous Galerkin method under minimal regularity assumptions. The main result is that sequences of discrete solutions weakly accumulate at weak solutions of the continuous problem. In order to deal with the non-conformity of the method and to avoid overpenalisation of jumps across interelement boundaries, the careful construction of a reflexive subspace of the space of bounded variation, which compactly embeds into $L^2(\Omega)$, and of a lifting operator, which is compatible with the nonlinear diffusion coefficient, are required. An equivalent skew-symmetric formulation of the convection and reaction terms of the nonlinear partial differential equation allows to avoid flux limitation and nonetheless leads to an unconditionally stable and convergent numerical method. Numerical experiments underline the robustness of the proposed algorithm

Statistical stopping criteria for automated screening in systematic reviews

Active learning for systematic review screening promises to reduce the human effort required to identify relevant documents for a systematic review. Machines and humans work together, with humans providing training data, and the machine optimising the documents that the humans screen. This enables the identification of all relevant documents after viewing only a fraction of the total documents. However, current approaches lack robust stopping criteria, so that reviewers do not know when they have seen all or a certain proportion of relevant documents. This means that such systems are hard to implement in live reviews. This paper introduces a workflow with flexible statistical stopping criteria, which offer real work reductions on the basis of rejecting a hypothesis of having missed a given recall target with a given level of confidence. The stopping criteria are shown on test datasets to achieve a reliable level of recall, while still providing work reductions of on average 17%. Other methods proposed previously are shown to provide inconsistent recall and work reductions across datasets

Vectorized Scenario Description and Motion Prediction for Scenario-Based Testing

Automated vehicles (AVs) are tested in diverse scenarios, typically specified by parameters such as velocities, distances, or curve radii. To describe scenarios uniformly independent of such parameters, this paper proposes a vectorized scenario description defined by the road geometry and vehicles' trajectories. Data of this form are generated for three scenarios, merged, and used to train the motion prediction model VectorNet, allowing to predict an AV's trajectory for unseen scenarios. Predicting scenario evaluation metrics, VectorNet partially achieves lower errors than regression models that separately process the three scenarios' data. However, for comprehensive generalization, sufficient variance in the training data must be ensured. Thus, contrary to existing methods, our proposed method can merge diverse scenarios' data and exploit spatial and temporal nuances in the vectorized scenario description. As a result, data from specified test scenarios and real-world scenarios can be compared and combined for (predictive) analyses and scenario selection.Comment: 6 pages, 7 figures, 3 table

Transfer Importance Sampling \unicode{x2013} How Testing Automated Vehicles in Multiple Test Setups Helps With the Bias-Variance Tradeoff

The promise of increased road safety is a key motivator for the development of automated vehicles (AV). Yet, demonstrating that an AV is as safe as, or even safer than, a human-driven vehicle has proven to be challenging. Should an AV be examined purely virtually, allowing large numbers of fully controllable tests? Or should it be tested under real environmental conditions on a proving ground? Since different test setups have different strengths and weaknesses, it is still an open question how virtual and real tests should be combined. On the way to answer this question, this paper proposes transfer importance sampling (TIS), a risk estimation method linking different test setups. Fusing the concepts of transfer learning and importance sampling, TIS uses a scalable, cost-effective test setup to comprehensively explore an AV's behavior. The insights gained then allow parameterizing tests in a more trustworthy test setup accurately reflecting risks. We show that when using a trustworthy test setup alone is prohibitively expensive, linking it to a scalable test setup can increase efficiency \unicode{x2013} without sacrificing the result's validity. Thus, the test setups' individual deficiencies are compensated for by their systematic linkage.Comment: 6 pages, 5 figures, 1 table, submitted to IEEE ITSC 202

Zum Stofftransport schwer flüchtiger Additive in Polymerbeschichtungen - Untersuchungen mit Hilfe der konvokalen Mikro-Raman-Spektroskopie

Schwerpunkt dieses Buchs ist es, den Einfluss von schwer flüchtigen Additiven auf die Mobilität von Lösemitteln zu quantifizieren sowie die Mobilität der Additive in Polymersystemen zu bestimmen. Ausgangspunkt hierfür war die Hypothese, dass sich Fick\u27sche Diffusionskoeffizienten einzelner Spezies in Polymersystemen durch Anpassung von Simulationsrechnungen an gemessene Beladungsprofile zu verschiedenen Zeitpunkten eines Konzentrationsausgleichsprozesses bestimmen lassen