666 research outputs found

    A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

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    In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

    Childhood loneliness as a predictor of adolescent depressive symptoms: an 8-year longitudinal study

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    Childhood loneliness is characterised by children’s perceived dissatisfaction with aspects of their social relationships. This 8-year prospective study investigates whether loneliness in childhood predicts depressive symptoms in adolescence, controlling for early childhood indicators of emotional problems and a sociometric measure of peer social preference. 296 children were tested in the infant years of primary school (T1 5 years of age), in the upper primary school (T2 9 years of age) and in secondary school (T3 13 years of age). At T1, children completed the loneliness assessment and sociometric interview. Their teachers completed externalisation and internalisation rating scales for each child. At T2, children completed a loneliness assessment, a measure of depressive symptoms, and the sociometric interview. At T3, children completed the depressive symptom assessment. An SEM analysis showed that depressive symptoms in early adolescence (age 13) were predicted by reports of depressive symptoms at age 8, which were themselves predicted by internalisation in the infant school (5 years). The interactive effect of loneliness at 5 and 9, indicative of prolonged loneliness in childhood, also predicted depressive symptoms at age 13. Parent and peer-related loneliness at age 5 and 9, peer acceptance variables, and duration of parent loneliness did not predict depression. Our results suggest that enduring peer-related loneliness during childhood constitutes an interpersonal stressor that predisposes children to adolescent depressive symptoms. Possible mediators are discussed

    Evaluation of a new arterial pressure-based cardiac output device requiring no external calibration

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    <p>Abstract</p> <p>Background</p> <p>Several techniques have been discussed as alternatives to the intermittent bolus thermodilution cardiac output (CO<sub>PAC</sub>) measurement by the pulmonary artery catheter (PAC). However, these techniques usually require a central venous line, an additional catheter, or a special calibration procedure. A new arterial pressure-based cardiac output (CO<sub>AP</sub>) device (FloTrac™, Vigileo™; Edwards Lifesciences, Irvine, CA, USA) only requires access to the radial or femoral artery using a standard arterial catheter and does not need an external calibration. We validated this technique in critically ill patients in the intensive care unit (ICU) using CO<sub>PAC </sub>as the method of reference.</p> <p>Methods</p> <p>We studied 20 critically ill patients, aged 16 to 74 years (mean, 55.5 ± 18.8 years), who required both arterial and pulmonary artery pressure monitoring. CO<sub>PAC </sub>measurements were performed at least every 4 hours and calculated as the average of 3 measurements, while CO<sub>AP </sub>values were taken immediately at the end of bolus determinations. Accuracy of measurements was assessed by calculating the bias and limits of agreement using the method described by Bland and Altman.</p> <p>Results</p> <p>A total of 164 coupled measurements were obtained. Absolute values of CO<sub>PAC </sub>ranged from 2.80 to 10.80 l/min (mean 5.93 ± 1.55 l/min). The bias and limits of agreement between CO<sub>PAC </sub>and CO<sub>AP </sub>for unequal numbers of replicates was 0.02 ± 2.92 l/min. The percentage error between CO<sub>PAC </sub>and CO<sub>AP </sub>was 49.3%. The bias between percentage changes in CO<sub>PAC </sub>(ΔCO<sub>PAC</sub>) and percentage changes in CO<sub>AP </sub>(ΔCO<sub>AP</sub>) for consecutive measurements was -0.70% ± 32.28%. CO<sub>PAC </sub>and CO<sub>AP </sub>showed a Pearson correlation coefficient of 0.58 (<it>p </it>< 0.01), while the correlation coefficient between ΔCO<sub>PAC </sub>and ΔCO<sub>AP </sub>was 0.46 (<it>p </it>< 0.01).</p> <p>Conclusion</p> <p>Although the CO<sub>AP </sub>algorithm shows a minimal bias with CO<sub>PAC </sub>over a wide range of values in an inhomogeneous group of critically ill patients, the scattering of the data remains relative wide. Therefore, the used algorithm (V 1.03) failed to demonstrate an acceptable accuracy in comparison to the clinical standard of cardiac output determination.</p
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