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BackgroundEmergency physicians can use a manual or an automated defibrillator to provide defibrillation of patients who had out-of-hospital cardiac arrest (OHCA). Performance of emergency physicians in identifying shockable rhythm with a manual defibrillator has been poorly explored whereas that of automated defibrillators is well known (sensitivity 0.91–1.00, specificity 0.96–0.99). We conducted this study to estimate the sensitivity/specificity and speed of shock/no-shock decision-making by prehospital emergency physicians for shockable or non-shockable rhythm, and their preference for manual versus automated defibrillation.MethodsWe developed a web application that simulates a manual defibrillator (https://simul-shock.firebaseapp.com/). In 2019, all (262) emergency physicians of six French emergency medical services were invited to participate in a study in which 60 ECG rhythms from real OHCA recordings were successively presented to the physicians for determination of whether they would or would not administer a shock. Time to decision was recorded. Answers were compared with a gold standard (concordant answers of three experts). We report sensitivity for shockable rhythms (decision to shock) and specificity for non-shockable rhythms (decision not to shock). Physicians were also asked whether they preferred manual or automated defibrillation.ResultsAmong 215 respondents, we were able to analyse results for 190 physicians. 57% of emergency physicians preferred manual defibrillation. Median (IQR) sensitivity for a shock delivery for shockable rhythm was 0.91 (0.81–1.00); median specificity for no-shock delivery for non-shockable rhythms was 0.91 (0.80–0.96). More precisely, sensitivities for shock delivery for ventricular tachycardia (VT) and coarse ventricular fibrillation (VF) were both 1.0 (1.0–1.0); sensitivity for fine VF was 0.6 (0.2–1). Specificity for not shocking a pulseless electrical activity (PEA) was 0.83 (0.72–0.86), and for asystole, specificity was 0.93 (0.86–1). Median speed of decision-making (in seconds) were: VT 2.0 (1.6–2.7), coarse VF 2.1 (1.7–2.9), asystole 2.4 (1.8–3.5), PEA 2.8 (2.0–4.2) and fine VF 2.8 (2.1–4.3).ConclusionsGlobal sensitivity and specificity were comparable with published automated external defibrillator studies. Shockable rhythms with the best clinical prognoses (VT and coarse VF) were very rapidly recognised with very good sensitivity. The decision-making for fine VF or asystole and PEA was less accurate.

Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. Yet in spite of extensive research, how they can learn through synaptic plasticity to carry out complex network computations remains unclear. We argue that two pieces of this puzzle were provided by experimental data from neuroscience. A mathematical result tells us how these pieces need to be combined to enable biologically plausible online network learning through gradient descent, in particular deep reinforcement learning. This learning method–called e-prop–approaches the performance of backpropagation through time (BPTT), the best-known method for training recurrent neural networks in machine learning. In addition, it suggests a method for powerful on-chip learning in energy-efficient spike-based hardware for artificial intelligence., Bellec et al. present a mathematically founded approximation for gradient descent training of recurrent neural networks without backwards propagation in time. This enables biologically plausible training of spike-based neural network models with working memory and supports on-chip training of neuromorphic hardware.

Background: Marseille is the second largest city in France. The Marseille Fire Brigade (BMPM) is the largest unity of the French Navy. This organization is in charge of rescue operations and medical intervention in the Marseille area. The aim of the study was to describe the epidemiology of interventions that required a physician to be present that were performed by the BMPM between the years of 2005 to 2017. Materials and methods: The statistical office database of the BMPM and the medical interventions forms (FIM) acquired from the BMPM medical ambulances (SMUR) archives were analysed from the years 2005 to 2017. Results: The BMPM performed a total of 2,375 interventions in the maritime environment between 2005 and 2017. A physician was necessary for intervention a total of 186 times. The extraction and analysis reports of 107 medical intervention forms found the BMPM archives revealed a significant number of interventions (67%) in the southern bay of Marseille and Frioul, specifically from the If and Planier islands. The majority of interventions (77%) took place within the 300m band. The most common cause of medical intervention was due to an accidental fall into the water, followed by boating (sailing and motor), and swimming. Drowning was the most common cause of mortality, consisting of 34% of all interventions. Diving accidents represented 14% of interventions. Trauma affected 22% of the study population and 83% of trauma patients were transported to the hospital under the supervision of a physician. Conclusions: Potential areas for improvement in the management of drowning victims are the use of Szpilman’s classification, sonography, and non-invasive ventilation. A recertification course for medical education training of BMPM doctors on the management of diving accidents could help to optimize the information recorded on FIM. Accident prevention training should be continued and reinforced when it comes to maritime activities.

Emulating spiking neural networks on analog neuromorphic hardware offers several advantages over simulating them on conventional computers, particularly in terms of speed and energy consumption. However, this usually comes at the cost of reduced control over the dynamics of the emulated networks. In this paper, we demonstrate how iterative training of a hardware-emulated network can compensate for anomalies induced by the analog substrate. We first convert a deep neural network trained in software to a spiking network on the BrainScaleS wafer-scale neuromorphic system, thereby enabling an acceleration factor of 10 000 compared to the biological time domain. This mapping is followed by the in-the-loop training, where in each training step, the network activity is first recorded in hardware and then used to compute the parameter updates in software via backpropagation. An essential finding is that the parameter updates do not have to be precise, but only need to approximately follow the correct gradient, which simplifies the computation of updates. Using this approach, after only several tens of iterations, the spiking network shows an accuracy close to the ideal software-emulated prototype. The presented techniques show that deep spiking networks emulated on analog neuromorphic devices can attain good computational performance despite the inherent variations of the analog substrate., 8 pages, 10 figures, submitted to IJCNN 2017

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 2017

International audience; Extracting invariant features in an un-supervised manner is crucial to perform complex computation such as object recognition, analyzing music or understanding speech. While various algorithms have been proposed to perform such a task, Slow Feature Analysis (SFA) uses time as a means of detecting those invariants, extracting the slowly time-varying components in the input signals. In this work, we address the question of how such an algorithm can be implemented by neurons, and apply it in the context of audio stimuli. We propose a projected gradient implementation of SFA that can be adapted to a Hebbian like learning rule dealing with biologically plausible neuron models. Furthermore, we show that a Spike-Timing Dependent Plasticity learning rule, shaped as a smoothed second derivative, implements SFA for spiking neurons. The theory is supported by numerical simulations, and to illustrate a simple use of SFA, we have applied it to auditory signals. We show that a single SFA neuron can learn to extract the tempo in sound recordings .