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Neural Metabolism In Vivo strives to offer a comprehensive and fundamental overview of cerebral metabolism by presenting leading-edge in vivo multimodal technology and its application in assessing neural activity, energy metabolism and neurotransmission in ...
Spiking Neuron Networks (SNNs) are often referred to as the 3rd generation of neural networks. They derive their strength and interest from an accurate modelling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs ...
INTRODUCTION: Contextual processing is often strongly deteriorated in schizophrenic patients as found, for example, in higher cognitive as well as lower visual paradigms. In visual detection tasks, impoverished contextual facilitation was attributed to abe ...
We present a biologically-inspired neural model addressing the problem of transformations across frames of reference in a posture imitation task. Our modeling is based on the hypothesis that imitation is mediated by two concurrent transformations selective ...
Adhesively-bonded joints, including double- and stepped- lap joints (DLJs and SLJs), were experimentally investigated under cyclic tensile loading. The joints were composed of pultruded GFRP laminates and epoxy adhesive. A critical stiffness was found for ...
Wireless Multi-Hop CSMA/CA Networks are challenging to analyze. On the one hand, their dynamics are complex and rather subtle effects may severely affect their performance. Yet, understanding these effects is critical to operate upper layer protocols, such ...
Many biological networks can maintain their function against single gene loss. However, the evolutionary mechanisms responsible for such robustness remain unclear. Here, we demonstrate that antagonistic host-parasite interactions can act as a selective pre ...
Nature / European Molecular Biology Organization2008
This paper investigates the use of a hierarchy of Neural Networks for performing data driven feature extraction. Two different hierarchical structures based on long and short temporal context are considered. Features are tested on two different LVCSR syste ...
This paper investigates the use of a hierarchy of Neural Networks for performing data driven feature extraction. Two different hierarchical structures based on long and short temporal context are considered. Features are tested on two different LVCSR syste ...
This paper presents an application of an artificial neural network to determine survival time of patients with a bladder cancer. Different learning methods have been investigated to find a solution, which is most optimal from a computational complexity poi ...