Results 91 to 100 of about 28,095 (262)

Hyperdimensional decoding of spiking neural networks

Neuromorphic Computing and Engineering
Abstract This work presents a novel spiking neural network (SNN) decoding method, combining SNNs with hyperdimensional computing (HDC). This decoding method is designed to achieve high accuracy, high noise robustness, low inference latency and low energy consumption. Compared to analogous architectures decoded with existing approaches,
Cedrick Kinavuidi, Luca Peres, Oliver Rhodes   +2 more
openaire   +3 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, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +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, Thomas Rduch, Salma Sherif Azer, Inge K. Herrmann   +3 more
wiley   +1 more source

ChronoPlastic Spiking Neural Networks

CoRR
21 pages, 6 ...
openaire   +2 more sources

In Situ Integrated Titanium Oxide Synaptic Phototransistor Enabling Multimodal Plasticity and Noise‐Robust Selective Attention

Advanced Materials Technologies, EarlyView.
An in situ integrated TiO2/SiOx/Al2O3 synaptic phototransistor couples ultraviolet and electrical stimuli within a scalable, CMOS‐compatible oxide stack. Multimodal plasticity, spike‐timing‐dependent learning, and bee‐inspired associative conditioning are achieved through trap‐mediated temporal dynamics.
Youngbin Yoon, Jaehee Lee, Jeongwook Kwon, Yongki Kim, Myunghun Shin, Jung Wook Lim   +5 more
wiley   +1 more source

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator

Frontiers in Neuroinformatics, 2017
Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units.
Jan Hahne, David Dahmen, Jannis Schuecker, Andreas Frommer, Matthias Bolten, Moritz Helias, Moritz Helias, Markus Diesmann, Markus Diesmann, Markus Diesmann   +9 more
doaj   +1 more source

Directional Latent Hybridization: Beyond Random Noise in Physics‐Informed Generative Inverse Design of Nonlinear Metamaterials

Advanced Materials Technologies, EarlyView.
A physics‐informed generative framework introduces Directional Latent Hybridization (DLH) for the deterministic inverse design of nonlinear metamaterials. By hybridizing dominant traits from parent geometries in the latent space, DLH overcomes the instabilities of stochastic models to ensure high structural precision at high densities.
Semin Ahn, Jun Young Choi, Sung‐Hoon Ahn   +2 more
wiley   +1 more source

Identifying Physical Interactions in Contact‐Based Robot Manipulation for Learning from Demonstration

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, Herman van der Kooij, Douwe Dresscher, Mark Vlutters   +3 more
wiley   +1 more source

A highly energy-efficient multi-core neuromorphic architecture for training deep spiking neural networks

Nature Communications
There is a growing necessity for edge training to adapt to dynamically changing environments. Neuromorphic computing represents a significant pathway for highly efficient intelligent computation in energy-constrained edges, but existing neuromorphic ...
Mingjing Li, Huihui Zhou, Xiaofeng Xu, Zhiwei Zhong, Puli Quan, Xueke Zhu, Yanyu Lin, Wenjie Lin, Xiaosha Li, Dong Wang, Junchao Zhang, Yunhao Ma, Xiaole Cui, Wei Wang, Qingyan Meng, Zhengyu Ma, Guoqi Li, Xiaoxin Cui, Yonghong Tian   +18 more
doaj   +1 more source

Spiking Neural Models of Neurons and Networks for Perception, Learning, Cognition, and Navigation: A Review

Brain Sciences
This article reviews and synthesizes highlights of the history of neural models of rate-based and spiking neural networks. It explains that theoretical and experimental results about how all rate-based neural network models, whose cells obey the membrane
Stephen Grossberg
doaj   +1 more source