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Helping the usefulness associated with exome sequencing in a quaternary treatment recommendation centre: book versions, clinical presentations along with analytic issues inside unusual neurogenetic illnesses.
At the core of the proposed model are different variants of Growth Transform dynamical systems that produce stable and interpretable population dynamics, irrespective of the network size and the type of neuronal connectivity (inhibitory or excitatory). In this paper, we present several examples where the proposed model has been configured to produce different types of single-neuron dynamics as well as population dynamics. In one such example, the network is shown to adapt such that it encodes the steady-state solution using a reduced number of spikes upon convergence to the optimal solution. Vismodegib inhibitor In this paper, we use this network to construct a spiking associative memory that uses fewer spikes compared to conventional architectures, while maintaining high recall accuracy at high memory loads.A growing body of work underlines striking similarities between biological neural networks and recurrent, binary neural networks. A relatively smaller body of work, however, addresses the similarities between learning dynamics employed in deep artificial neural networks and synaptic plasticity in spiking neural networks. The challenge preventing this is largely caused by the discrepancy between the dynamical properties of synaptic plasticity and the requirements for gradient backpropagation. Learning algorithms that approximate gradient backpropagation using local error functions can overcome this challenge. Here, we introduce Deep Continuous Local Learning (DECOLLE), a spiking neural network equipped with local error functions for online learning with no memory overhead for computing gradients. DECOLLE is capable of learning deep spatio temporal representations from spikes relying solely on local information, making it compatible with neurobiology and neuromorphic hardware. Synaptic plasticity rules are derived systematically from user-defined cost functions and neural dynamics by leveraging existing autodifferentiation methods of machine learning frameworks. We benchmark our approach on the event-based neuromorphic dataset N-MNIST and DvsGesture, on which DECOLLE performs comparably to the state-of-the-art. DECOLLE networks provide continuously learning machines that are relevant to biology and supportive of event-based, low-power computer vision architectures matching the accuracies of conventional computers on tasks where temporal precision and speed are essential.Introduction Tinnitus is a complex symptom requiring a thorough multidisciplinary assessment to construct an individual's tinnitus profile. The Antwerp University Hospital hosts a tertiary tinnitus clinic providing intensive, multidisciplinary tinnitus care in the form of combinational psychological treatment with either Tinnitus Retraining Therapy (TRT)/Cognitive Behavioral Therapy (CBT) or TRT/eye movement desensitization and reprocessing therapy (EMDR), high-definition transcranial direct current stimulation (HD-tDCS), and physical therapy treatment (in cases of somatic influence of the neck or the temporomandibular area). Several factors may contribute to therapy effect of which the role of gender has recently gained more interest. As such, the current manuscript explores gender differences in the outcome of different tinnitus treatments. Methods Data on treatment outcome of four distinct tinnitus treatments (1. HD-tDCS; 2. orofacial physical therapy; 3. combination TRT + CBT; and 4. combination TRT + EMDbaseline levels (p = 0.0138). Conclusion Our data suggest that male and female tinnitus patients seem to react differently to different therapy options. We strongly encourage further prospective studies to discern the relevance of gender in therapy outcome.Auditory processing disorder (APD) is a specific deficit in the processing of auditory information along the central auditory nervous system. It is characterized mainly by deficits in speech in noise recognition. APD children may also present with deficits in processing of auditory rhythm. Rhythmic neural entrainment is commonly present in perception of both speech and music, while auditory rhythmic priming of speech in noise has been known to enhance recognition in typical children. Here, we test the hypothesis that the effect of rhythmic priming is compromised in APD children, and further assessed for correlations with verbal and non-verbal auditory processing and cognition. Forty APD children and 33 neurotypical ones were assessed through (a) WRRC, a test measuring the effects of rhythmic priming on speech in noise recognition, (b) a battery of auditory processing tests, commonly used in APD diagnosis, and (c) two cognitive tests, assessing working memory and auditory attention respectively. Findings revealed that (a) the effect of rhythmic priming on speech in noise recognition is absent in APD children, (b) it is linked to non-verbal auditory processing, and (c) it is only weakly dependent on cognition. We discuss these findings in light of Dynamic Attention Theory, neural entrainment and neural oscillations and suggest that these functions may be compromised in APD children. Further research is needed (a) to explore the nature of the mechanics of rhythmic priming on speech in noise perception and why the effect is absent in APD children, (b) which other mechanisms related to both rhythm and language are also affected in this population, and (c) whether music/rhythm training can restore deficits in rhythm effects.Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has motivated the search for novel computing architectures targeting this application. A computational memory unit with nanoscale resistive memory devices organized in crossbar arrays could store the synaptic weights in their conductance states and perform the expensive weighted summations in place in a non-von Neumann manner. However, updating the conductance states in a reliable manner during the weight update process is a fundamental challenge that limits the training accuracy of such an implementation. Here, we propose a mixed-precision architecture that combines a computational memory unit performing the weighted summations and imprecise conductance updates with a digital processing unit that accumulates the weight updates in high precision. A combined hardware/software training experiment of a multilayer perceptron based on the proposed architecture using a phase-change memory (PCM) array achieves 97.
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