Results 81 to 90 of about 25,328 (252)
Advanced Materials Technologies, EarlyView.Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu +8 more
wiley +1 more source
SHORT TERM MEMORY IN A NETWORK OF SPIKING NEURONS [PDF]
Connectionist Models of Cognition and Perception, 2002 A distributed connectionist model of spiking neurons (INFERNET) is used to simulate various aspects of Short Term Memory. In INFERNET, short term memory is the transient activation of long term memory elements. This single store model has a human-like performance in short term memory span tasks, but also displays serial position effects, similarity ...
openaire +2 more sources
Recent Advances of Slip Sensors for Smart Robotics
Advanced Materials Technologies, EarlyView.This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang +8 more
wiley +1 more source
At Home Detection of Ovarian Health Biomarker in Menstruation Blood
Advanced Materials Technologies, EarlyView.A lateral flow assay enables the detection of anti‐Müllerian hormone directly in unprocessed menstrual blood using silica‐gold nanoshells and smartphone‐assisted machine learning analysis. The platform supports decentralized, user‐operated testing in wearable and dipstick formats, highlighting the potential of menstrual blood as a non‐invasive matrix ...
Lucas Dosnon +3 more
wiley +1 more source
Implementing Signature Neural Networks with Spiking Neurons
Frontiers in Computational Neuroscience, 2016 Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes.
José Luis Carrillo-Medina +1 more
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Advanced Robotics Research, EarlyView.Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek +3 more
wiley +1 more source
Applied SciencesSpiking neural networks (SNNs) aim to simulate human neural networks with biologically plausible neurons. However, conventional SNNs based on point neurons ignore the inherent dendritic computation of biological neurons. Additionally, these point neurons
Qian Zhou, Wenjie Wang, Mengting Qiao
doaj +1 more source
Cell Microscopic Segmentation with Spiking Neuron Networks [PDF]
, 2010 Spiking Neuron Networks (SNNs) overcome the computational power of neural networks made of thresholds or sigmoidal units. Indeed, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks.
Boudjelal Meftah +3 more
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Advanced Robotics Research, EarlyView.This review identifies key design considerations for insect‐inspired microrobots capable of multimodal locomotion. To draw inspiration, biological and robotic strategies for moving in air, on water surfaces, and underwater are examined, along with approaches for crossing the air–water interface.
Mija Jovchevska +2 more
wiley +1 more source
eLife, 2016 The striatum and the subthalamic nucleus (STN) constitute the input stage of the basal ganglia (BG) network and together innervate BG downstream structures using GABA and glutamate, respectively.
Marc Deffains +5 more
doaj +1 more source