Results 231 to 240 of about 68,095 (317)

Three-factor learning in spiking neural networks: An overview of methods and trends from a machine learning perspective. [PDF]

Patterns (N Y)
Mazurek S, Caputa J, Argasiński JK, Wielgosz M.   +3 more
europepmc   +1 more source

Tunneling‐Controlled Fusion of Short‐ and Long‐Term Memory in SiO2/HfO2‐Based Neuromorphic Device for Time‐Series Prediction

Advanced Science, EarlyView.
Based on trap‐assisted tunneling, the devices can fuse STM/LTM, where the low switching energy of 1 pJ and stable low‐power retention (0.2 % loss ratio and 3.05 × 10−11 W) is achieved. Training in a long short‐term memory network it allows to analysis time‐series data and then makes precise long‐term predictions with an error ratio of 4.465 ...
Chengdong Yang, Lihua Xu, Linlin Su, Yue Wu   +3 more
wiley   +1 more source

BN-SNN: Spiking neural networks with bistable neurons for object detection. [PDF]

PLoS One
Muhammad Yasir S, Kim H.
europepmc   +1 more source

H⁺ Exchange‐Driven ppb‐Level and High‐Selective Formaldehyde Detection at Room Temperature for Environmental and Clinical Applications

Advanced Science, EarlyView.
This article presents an innovative room‐temperature formaldehyde sensor based on H⁺ exchange method, achieving ppb‐level detection limit and exceptional selectivity. The study highlights its practical potential for real‐time environmental monitoring and clinical diagnostic applications.
Lubing Cai, Mengyang Pang, Zhaosong Liu, Yanfei Li, Jiani Li, Zhaorui Zhang, Fengshuang Zheng, Chao Li, Ang Zheng, Xuemin Zhang   +9 more
wiley   +1 more source

Unsupervised post-training learning in spiking neural networks. [PDF]

Sci Rep
Naderi R, Rezaei A, Amiri M, Peremans H.
europepmc   +1 more source

A large-scale spiking neural networks emulation architecture

, 2014
Vito Pirrone
openalex   +1 more source

Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks

Yufei Guo, Yuanpei Chen, Xiaode Liu, Weihang Peng, Yuhan Zhang, Xuhui Huang, Zhe Ma   +6 more
openalex   +2 more sources

Generating Dynamic Structures Through Physics‐Based Sampling of Predicted Inter‐Residue Geometries

Advanced Science, EarlyView.
While static structure prediction has been revolutionized, modeling protein dynamics remains elusive. trRosettaX2‐Dynamics is presented to address this challenge. This framework leverages a Transformer‐based network to predict inter‐residue geometric constraints, guiding conformation generation via physics‐based iterative sampling. The resulting method
Chenxiao Xiang, Wenkai Wang, Zhenling Peng, Jianyi Yang   +3 more
wiley   +1 more source

Advancing spatio-temporal processing through adaptation in spiking neural networks. [PDF]

Nat Commun
Baronig M, Ferrand R, Sabathiel S, Legenstein R.   +3 more
europepmc   +1 more source

Artificial grammar recognition using two spiking neural networks

, 2009
Philip Cavaco, Baran Çürüklü, Magnus Karl-Petersson   +2 more
openalex   +1 more source