Accelerated physical emulation of Bayesian inference in spiking neural networks

The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.Comment: This preprint has been published 2019 November 14. Please cite as: Kungl A. F. et al. (2019) Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks. Front. Neurosci. 13:1201. doi: 10.3389/fnins.2019.0120

Pattern representation and recognition with accelerated analog neuromorphic systems

Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks. In this paper, we review several possibilites to reverse map these architectures to biologically more realistic spiking networks with the aim of emulating them on fast, low-power neuromorphic hardware. Since many of these devices employ analog components, which cannot be perfectly controlled, finding ways to compensate for the resulting effects represents a key challenge. Here, we discuss three different strategies to address this problem: the addition of auxiliary network components for stabilizing activity, the utilization of inherently robust architectures and a training method for hardware-emulated networks that functions without perfect knowledge of the system's dynamics and parameters. For all three scenarios, we corroborate our theoretical considerations with experimental results on accelerated analog neuromorphic platforms.Comment: accepted at ISCAS 201

Detailed Characterization of Small Extracellular Vesicles from Different Cell Types Based on Tetraspanin Composition by ExoView R100 Platform

Small extracellular vesicles (sEV) hold enormous potential as biomarkers, drug carriers, and therapeutic agents. However, due to previous limitations in the phenotypic characterization of sEV at the single vesicle level, knowledge of cell type-specific sEV signatures remains sparse. With the introduction of next-generation sEV analysis devices, such as the single-particle interferometric reflectance imaging sensor (SP-IRIS)-based ExoView R100 platform, single sEV analyses are now possible. While the tetraspanins CD9, CD63, and CD81 were generally considered pan-sEV markers, it became clear that sEV of different cell types contain several combinations and amounts of these proteins on their surfaces. To gain better insight into the complexity and heterogeneity of sEV, we used the ExoView R100 platform to analyze the CD9/CD63/CD81 phenotype of sEV released by different cell types at a single sEV level. We demonstrated that these surface markers are sufficient to distinguish cell-type-specific sEV phenotypes. Furthermore, we recognized that tetraspanin composition in some sEV populations does not follow a random pattern. Notably, the tetraspanin distribution of sEV derived from mesenchymal stem cells (MSCs) alters depending on cell culture conditions. Overall, our data provide an overview of the cell-specific characteristics of sEV populations, which will increase the understanding of sEV physiology and improve the development of new sEV-based therapeutic approaches

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Comparison of T-wave alternans measurement during various pacing modes in patients with dual chamber pacemaker systems

0 Titel u. Inhaltsverzeichnis X 1 Einleitung 2 2 Methodik 5 2.1 Patienten-Kollektiv 5 2.2 T-Wellen-Alternans-Messung 7 2.3 Studienprotokoll 11 2.3.1 Störfaktoren 12 2.3.2 Software-Analyse 13 2.3.3 Definitionen 14 2.3.4 Klinische Parameter 15 2.4 Statistik 15 3 Ergebnisse 17 3.1 Klinische Befunde 17 3.2 Ergebnisse bei AAI-Stimulation 17 3.3 Ergebnisse bei VVI-Stimulation 20 3.4 Ergebnisse bei DDD-Stimulation 22 3.5 Vergleich der Ergebnisse bei verschiedenen Stimulationsarten 22 3.6 Zusammenhang zwischen Ejektionsfraktion, linksventrikulärem enddiastolischen Diameter und auswertbaren Ergebnissen bei AAI-Stimulation 37 3.7 Einfluß von Fusionsschlägen auf die Ergebnisse 42 3.8 Einfluß der Herzfrequenz auf die Ergebnisse 44 3.9 Einfluß einer koronaren Herzerkrankung auf die Ergebnisse 49 4 Diskussion 52 5 Zusammenfassung 63 6 Literaturverzeichnis 65Der T-Wellen-Alternans (TWA) ist ein neuer Parameter zur Risikostratifikation hinsichtlich maligner ventrikulärer Tachyarrhythmien und des plötzlichen Herztodes. Zur Bestimmung eines TWA ist es methodisch erforderlich, daß eine bestimmte Schwellenherzfrequenz (im allgemeinen 105/min) erreicht wird. Bisher wurde zur Erhöhung der Herzfrequenz als Goldstandard die Fahrrad-Ergometrie oder eine atriale Stimulation während elektrophysiologischer Untersuchungen angewandt. Entsprechend könnte bei Patienten mit Herzschrittmachern eine TWA- Bestimmung durch eine vorübergehende Umprogrammierung mit Erhöhung der Herzfrequenz auf die Schwellenfrequenz von 105 Schläge/min erfolgen. Unklar ist allerdings, ob bei Patienten mit einem sequentiellen Schrittmachersystem (DDD-Stimulation) die TWA-Bestimmung bei atrialer Stimulation im AAI-Modus jener bei VVI-Stimulation oder DDD-Stimulation vergleichbar ist. Dieses wäre jedoch die Voraussetzung dafür, daß eine TWA-Bestimmung auch bei Patienten mit höhergradigem AV-Block und DDD-Schrittmacher möglich wäre. Untersuchungen zur Übereinstimmung von TWA-Messungen bei unterschiedlichen Stimulationsarten liegen jedoch nicht vor. Ziel dieser Studie war es deshalb, bei Patienten mit DDD-Schrittmacher unterschiedliche Stimulationsmodi (AAI-Stimulation, VVI- Stimulation und DDD-Stimulation) zur TWA-Messung anzuwenden und die TWA- Befunde bei den verschiedenen Stimulationsarten zu vergleichen. Die TWA- Messung erfolgte mit dem CH 2000 System der Firma Cambridge Heart. Die Stimulation erfolgte zunächst im AAI-Modus über 3 Minuten mit einer Herzfrequenz von 105 Schlägen/min, im Falle eines bei geringerer Herzfrequenz eintretenden AV-Blocks wurde bei AAI-Stimulation mit der höchstmöglichen Frequenz stimuliert. Darauf folgend wurde mit der gleichen oder ebenfalls mit der höchstmöglichen Frequenz 3 Minuten bei VVI- und anschließend bei DDD- Stimulation gemessen. Die Kriterien für einen positiven TWA waren ein anhaltender Alternans über mindestens eine Minute mit einer TWA-Amplitude von >= 1,9 µV. Die Bewertung erfolgte durch einen in der TWA-Beurteilung erfahrenen Untersucher, dem die Stimulationsart nicht ersichtlich war. Das TWA-Ergebnis wurde als positiv, negativ oder nicht bestimmbar eingestuft. Es wurden 63 Patienten mit DDD-Schrittmacher in die Studie aufgenommen. Eine AAI- Stimulation konnte bei 56 Patienten durchgeführt werden, wobei 12/56 (21%) einen positiven, 28/56 (50%) einen negativen und 16/56 (29%) einen nicht bestimmbaren TWA-Befund aufwiesen. Die VVI-Stimulation konnte bei 63 Patienten, die DDD-Stimulation bei 62 Patienten vorgenommen werden. Die Verteilung der positiven, negativen und nicht bestimmbaren TWA-Befunde lag bei VVI-Stimulation bei 11/63 (17%), 26/63 (41%) und 26/63 (41%) und bei DDD- Stimulation bei 15/62 (24%), 28/62 (45%) und 19/62 (31%). Beim Vergleich zwischen den Stimulationsarten wiesen von den insgesamt 27 Patienten, die sowohl bei AAI- als auch bei VVI-Stimulation eindeutig positive oder negative TWA-Befunde hatten, 8 Patienten (30%) widersprüchliche TWA-Befunde auf. Der statistische Vergleich ergab dabei keine Abhängigkeit zwischen den TWA- Befunden bei den beiden Stimulationsmodi. Im Vergleich von AAI- und DDD- Stimulation gab es bei 4 von 27 (15%) Patienten widersprüchliche TWA-Befunde. Hier bestand eine statistisch signifikante Abhängigkeit zwischen den Befunden. Im Vergleich von VVI- und DDD-Stimulation bestand bei 10/29 widersprüchlichen TWA-Befunden (34%) keine nachweisbare Abhängigkeit zwischen beiden Stimulationsmodi. Diese Daten zeigen, daß bei Patienten mit DDD-Schrittmacher eine TWA-Messung während VVI-Stimulation nicht vergleichbar ist mit einer TWA- Bestimmung, die bei AAI-Stimulation erfolgt. Die VVI-Stimulation sollte daher nicht zur TWA-Bestimmung herangezogen werden. Die DDD-Stimulation ist der AAI- Stimulation dagegen eher vergleichbar. Allerdings sollte trotz statistisch signifikanten Nachweises einer Abhängigkeit zwischen beiden Stimulationsmodi eine TWA-Bewertung bei DDD-Stimulation nur zurückhaltend erfolgen, da 15% der Befunde sich in der vorliegenden Arbeit widersprochen haben und in fast einem Drittel der bei AAI-Stimulation auswertbaren Fälle eine TWA-Beurteilung bei DDD-Stimulation nicht möglich war.T-wave alternans (TWA) is a new method for the risk stratification of malignant ventricular tachyarrhythmias and sudden cardiac death. For measurement of TWA a specific heart rate threshold (most often 105 bpm) has to be reached. Until now exercise testing and atrial stimulation during electrophysiological testing have been used as a gold-standard. In pacemaker patients heart rate increase for TWA-measurement can also be achieved by means of reprogramming the pacemaker. It is not known whether the TWA result by means of atrial stimulation in AAI-mode is comparable to that during VVI- or DDD-Stimulation in patients with a dual chamber pacemaker system (DDD- stimulation). If this would be the case TWA-measurements would be possible in patients with high-degree AV-block and an implanted DDD-pacemaker. The aim of this study was to compare different modes of stimulation (AAI-, VVI- and DDD- stimulation) for TWA-measurement in pacemaker patients. Microvolt level TWA was measured using the Cambridge Heart method. ECG-recordings during atrial stimulation in AAI-mode were obtained for 3 minutes at a heart rate of 105 bpm. If this heart rate could not be achieved due to Wenckebach AV-block at a lower rate, stimulation was performed at the highest possible heart rate. Thereafter, ECG-recordings in the VVI-mode and in the DDD-mode were obtained for 3 minutes with either the same heart rate or also with the highest possible heart rate. TWA was defined positive if V(Alt) increased above 1.9 µV for at least one minute and negative if it did not meet the criteria for being positive and the maximum negative heart rate reached >= 90 bpm. TWA was defined indeterminate if it could not be definitely classified as either positive or negative. Results: There were 63 patients with DDD-pacemaker enrolled. TWA was measurable during AAI-mode pacing in 56 patients. Twelve out of 56 patients (21%) were TWA positive, 28/56 (50%) TWA negative and 16/56 (29%) TWA indeterminate. In VVI-mode TWA could be measured in 63 patients and DDD-mode pacing was performed in 62 patients. During VVI stimulation 11/63 (17%) cases were TWA positive while 26/63 (41%) were TWA negative and 26/63 (41%) TWA indeterminate. During DDD-stimulation 15/62 cases (24%) were TWA positive, 28/62 (45%) TWA negative and 19/62 (31%) TWA indeterminate. Comparing the stimulation modes 8/27 patients (30%) with either positive or negative TWA results during both AAI- and VVI-pacing had contradictory results without statistical significant dependence. Likewise, there were 4/27 (15%) contradictory results in AAI- and DDD-mode, whereas a statistical significant dependence of the results could be shown. The comparison of VVI- and DDD- stimulation with a number of 10/29 (34%) contradictory results showed no statistical dependence. The data shows that a TWA-measurement during VVI- stimulation is not comparable to those during AAI-stimulation in patients with DDD-pacemaker. VVI-stimulation should therefore not be used to measure TWA. DDD-stimulation shows a higher concordance when compared to AAI-stimulation. However, a TWA-measurement during DDD stimulation should only be used carefully since there were 15% contradictory results in this study and in nearly one third of the cases with evaluated measurements during AAI- stimulation an evaluation of DDD-stimulation was not possible

Understanding the Impact of Child, Intervention, and Family Factors on Developmental Trajectories of Children with Hearing Loss at Preschool Age: Design of the AChild Study

Children with hearing loss and their families represent a large variety with regard to their auditory, medical, psychological, and family resource characteristics. Despite recent advances, developmental outcomes are still below average, with a significant proportion of variety remaining unexplained. Furthermore, there is a lack of studies including the whole diversity of children with hearing loss. The AChild study (Austrian Children with Hearing Impairment—Longitudinal Databank) uses an epidemiological longitudinal design including all children living in Upper and Lower Austria with a permanent uni- or bilateral hearing loss below the age of 6 years, irrespective of additional disabilities, family language, and family resources. The demographic characteristics of the first 126 children enrolled in the study showed that about half of the children are either children with additional disabilities (31%) and/or children not growing up with the majority language (31.7%) that are usually excluded from comprehensive longitudinal studies. AChild aims for a characterization of the total population of young children with hearing loss including developmental outcomes. Another goal is the identification of early predictors of developmental trajectories and family outcomes. In addition to child-related predictors the examination of family–child transactions malleable by family-centred early intervention is of particular interest. The study is designed as participatory including parent representation atall stages. Measures have been chosen, following other large population-based studies in order to gain comparability and to ensure international data pooling