Results 91 to 100 of about 68,095 (317)
Superconducting Nanowire Spiking Element for Neural Networks [PDF]
, 2020 Emily Toomey +5 more
openalex +1 more source
Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks
, 2019 The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability.
Choe, Hyeokjun +3 more
core +1 more source
Advanced Functional Materials, EarlyView.A van der Waals optoelectronic synaptic device based on a ReS2/WSe2 heterostructure and oxygen‐treated h‐BN is presented, which enables both positive and negative PSCs through photocarrier polarity reversal. Bidirectional plasticity arises from gate‐tunable band bending and charge trapping‐induced quasi‐doping.
Hyejin Yoon +9 more
wiley +1 more source
SNNAX - Spiking Neural Networks in JAX
2024 International Conference on Neuromorphic Systems (ICONS)Spiking Neural Networks (SNNs) simulators are essential tools to prototype biologically inspired models and neuromorphic hardware architectures and predict their performance. For such a tool, ease of use and flexibility are critical, but so is simulation speed especially given the complexity inherent to simulating SNN.
Lohoff, Jamie +2 more
openaire +3 more sources
, 2020 Utilizing quantum computers to deploy artificial neural networks (ANNs) will bring the potential of significant advancements in both speed and scale. In this paper, we propose a kind of quantum spike neural networks (SNNs) as well as comprehensively evaluate and give a detailed mathematical proof for the quantum SNNs, including its successful ...
Chen, Yanhu +4 more
openaire +2 more sources
Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics
Advanced Functional Materials, EarlyView.Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha +18 more
wiley +1 more source
Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems
, 2013 Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification.
Cauwenberghs, Gert +4 more
core +1 more source
Advanced Functional Materials, EarlyView.This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source
Spike-based computation using classical recurrent neural networks
Neuromorphic Computing and EngineeringSpiking neural networks (SNNs) are a type of artificial neural networks in which communication between neurons is only made of events, also called spikes.
Florent De Geeter +2 more
doaj +1 more source
Advanced Functional Materials, EarlyView.In this study, the preparation techniques for silver‐based gas diffusion electrodes used for the electrochemical reduction of carbon dioxide (eCO2R) are systematically reviewed and compared with respect to their scalability. In addition, physics‐based and data‐driven modeling approaches are discussed, and a perspective is given on how modeling can aid ...
Simon Emken +6 more
wiley +1 more source