Results 11 to 20 of about 3,555,912 (299)

Spark: modular spiking neural networks [PDF]

Frontiers in Artificial Intelligence
Nowadays, neural networks act as a synonym for artificial intelligence. Present neural network models, although remarkably powerful, are inefficient both in terms of data and energy.
Mario Franco, Carlos Gershenson
doaj   +4 more sources

Spiking neural networks for nonlinear regression

Royal Society Open Science, 2023
Spiking neural networks (SNN), also often referred to as the third generation of neural networks, carry the potential for a massive reduction in memory and energy consumption over traditional, second-generation neural networks. Inspired by the undisputed
Alexander Henkes, Jason K. Eshraghian, Henning Wessels   +2 more
doaj   +9 more sources

Accelerating spiking neural networks with photonic reconfigurable devices [PDF]

Nature Communications
Spiking neural networks face hardware limitations as conventional architectures exhibit low array utilization, underperforming GPU-driven artificial neural networks in vision tasks. We present a programmable spiking neurocomputing architecture using CMOS-
Chen Lu, Kangli Xu, Jiaming Liu, Yuanhong Ding, Jiajie Yu, Jieru Song, Jialin Meng, Tianyu Wang, Shuming Guo, Xingcheng Jin, Hao Zhu, Qingqing Sun, David Wei Zhang, Ningsheng Xu, Yibo Fan, Lin Chen   +15 more
doaj   +2 more sources

Spiking Neural Networks and Their Applications: A Review

Brain Sciences, 2022
The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs.
Kashu Yamazaki, Viet-Khoa Vo-Ho, Darshan Bulsara, Ngan Le   +3 more
doaj   +3 more sources

Attention Spiking Neural Networks

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
18 pages, 8 figures, Under ...
Yonghong Tian, Lei Deng, Guoqi Li
exaly   +6 more sources

Federated training of spiking neural networks on edge hardware for audio processing [PDF]

Frontiers in Neuroscience
Spiking Neural Networks have caught significant attention recently for their potential for energy-efficient computation on neuromorphic hardware and their event-driven processing.
Swaroop S. Kaimal, Ashwin JB, S. Sofana Reka, Prakash Venugopal   +3 more
doaj   +2 more sources

Efficient event-based delay learning in spiking neural networks [PDF]

Nature Communications
Spiking Neural Networks compute using sparse communication and are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks.
Balázs Mészáros, James C. Knight, Thomas Nowotny   +2 more
doaj   +2 more sources

Integrating Non-spiking Interneurons in Spiking Neural Networks [PDF]

Frontiers in Neuroscience, 2021
Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware.
Beck Strohmer, Rasmus Karnøe Stagsted, Poramate Manoonpong, Leon Bonde Larsen   +3 more
doaj   +5 more sources

Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks [PDF]

Scientific Reports
Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI).
Aaditya Joshi, Paramveer Singh Matharu, Lokesh Malviya, Manoj Kumar, Akshay Jadhav   +4 more
doaj   +2 more sources

BIASNN: a biologically inspired attention mechanism in spiking neural networks for image classification [PDF]

Scientific Reports
Spiking Neural Networks (SNNs), designed to more accurately model the brain’s neurobiological processes, have been proposed as energy-efficient alternatives to conventional Artificial Neural Networks (ANNs), which typically incur high computational and ...
Kevin Takala, Wachirawut Thamviset, Sartra Wongthanavasu   +2 more
doaj   +2 more sources