Results 121 to 130 of about 60,411 (286)

Configuring spiking neural network training algorithms [PDF]

, 2017
Spiking neural networks, based on biologically-plausible neurons with temporal information coding, are provably more powerful than widely used artificial neural networks based on sigmoid neurons (ANNs).
Mustari, Mst Mausumi Sabnam
core  

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

Low‐Temperature Processed Optoelectronic Synapses With Enhanced Responsivity for Edge‐Oriented In‐Sensor Reservoir Computing

Advanced Materials Technologies, EarlyView.
Image pixels are converted into optical pulse sequences to stimulate the optoelectronic synaptic device, generating dynamic responses that form high‐dimensional features. These features improve classification efficiency and demonstrate strong potential for neuromorphic edge computing systems.
Jo‐Lin Chen, Shi‐Qian Guo, Yu‐Chuan Chiu, Po‐Tsun Liu   +3 more
wiley   +1 more source

RFI detection with spiking neural networks

Publications of the Astronomical Society of Australia
AbstractDetecting and mitigating radio frequency interference (RFI) is critical for enabling and maximising the scientific output of radio telescopes. The emergence of machine learning (ML) methods capable of handling large datasets has led to their application in radio astronomy, particularly in RFI detection.
N.J. Pritchard, A. Wicenec, M. Bennamoun, R. Dodson   +3 more
openaire   +2 more sources

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

Multimodal Locomotion in Insect‐Inspired Microrobots: A Review of Strategies for Aerial, Surface, Aquatic, and Interfacial Motion

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, Kaushik Jayaram, Nicole Xu   +2 more
wiley   +1 more source

SNNtrainer3D: Training Spiking Neural Networks Using a User-Friendly Application with 3D Architecture Visualization Capabilities

Applied Sciences
Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks ...
Sorin Liviu Jurj, Sina Banasaz Nouri, Jörg Strutwolf   +2 more
doaj   +1 more source

Auditory–Tactile Congruence for Synthesis of Adaptive Pain Expressions in RoboPatients

Advanced Robotics Research, EarlyView.
In this work, we explore auditory–tactile congruence for synthesizing adaptive vocal pain expressions in robopatients. Using a robopatient platform that integrates vocal pain sounds with palpation forces, we conducted 7680 trials across 20 participants.
Saitarun Nadipineni, Chapa Sirithunge, Yue Xie, Fumiya Iida, Thilina Dulantha Lalitharatne   +4 more
wiley   +1 more source

A State‐Adaptive Koopman Control Framework for Real‐Time Deformable Tool Manipulation in Robotic Environmental Swabbing

Advanced Robotics Research, EarlyView.
This work presents a state‐adaptive Koopman linear quadratic regulator framework for real‐time manipulation of a deformable swab tool in robotic environmental sampling. By combining Koopman linearization, tactile sensing, and centroid‐based force regulation, the system maintains stable contact forces and high coverage across flat and inclined surfaces.
Siavash Mahmoudi, Amirreza Davar, Dongyi Wang   +2 more
wiley   +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