Zobrazeno 1 - 10
of 24 476
pro vyhledávání: '"spiking neural network"'
Autor:
Lia, Ruixin, Zhaoa, Guoxu, Muir, Dylan Richard, Ling, Yuya, Burelo, Karla, Khoei, Mina, Wang, Dong, Xing, Yannan, Qiao, Ning
Publikováno v:
Computers in Biology and Medicine(2024), 183, 109225
Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficient diagnosis. In this study, we aimed to detect interictal and ictal p
Externí odkaz:
http://arxiv.org/abs/2410.16613
Autor:
Cao, Jiahang, Sun, Mingyuan, Wang, Ziqing, Cheng, Hao, Zhang, Qiang, Zhou, Shibo, Xu, Renjing
Event-based cameras are attracting significant interest as they provide rich edge information, high dynamic range, and high temporal resolution. Many state-of-the-art event-based algorithms rely on splitting the events into fixed groups, resulting in
Externí odkaz:
http://arxiv.org/abs/2410.02249
Gradient descent computed by backpropagation (BP) is a widely used learning method for training artificial neural networks but has several limitations: it is computationally demanding, requires frequent manual tuning of the network architecture, and
Externí odkaz:
http://arxiv.org/abs/2410.00745
Spiking neural network (SNN) has emerged as a promising paradigm in computational neuroscience and artificial intelligence, offering advantages such as low energy consumption and small memory footprint. However, their practical adoption is constraine
Externí odkaz:
http://arxiv.org/abs/2410.08229
Convolutional neural network (CNN) performs well in Hyperspectral Image (HSI) classification tasks, but its high energy consumption and complex network structure make it difficult to directly apply it to edge computing devices. At present, spiking ne
Externí odkaz:
http://arxiv.org/abs/2409.11619
Brain-computer interfaces (BCIs) are an advanced fusion of neuroscience and artificial intelligence, requiring stable and long-term decoding of neural signals. Spiking Neural Networks (SNNs), with their neuronal dynamics and spike-based signal proces
Externí odkaz:
http://arxiv.org/abs/2410.03533
Autor:
Kiselev, Mikhail
In the present paper, it is shown how the columnar/layered CoLaNET spiking neural network (SNN) architecture can be used in supervised learning image classification tasks. Image pixel brightness is coded by the spike count during image presentation p
Externí odkaz:
http://arxiv.org/abs/2409.07833
Autor:
Kiselev, Mikhail
In the present paper, I describe a spiking neural network (SNN) architecture which, can be used in wide range of supervised learning classification tasks. It is assumed, that all participating signals (the classified object description, correct class
Externí odkaz:
http://arxiv.org/abs/2409.01230
Spiking Neural Networks (SNNs), with their inherent recurrence, offer an efficient method for processing the asynchronous temporal data generated by Dynamic Vision Sensors (DVS), making them well-suited for event-based vision applications. However, e
Externí odkaz:
http://arxiv.org/abs/2411.02854
Tiny Machine Learning (TinyML) has become a growing field in on-device processing for Internet of Things (IoT) applications, capitalizing on AI algorithms that are optimized for their low complexity and energy efficiency. These algorithms are designe
Externí odkaz:
http://arxiv.org/abs/2411.01628