Spiking Neural Network简述 传统神经网络包括现存的各种以perceptron为基本单元的拓扑变种, 比如卷积神经网络系列(CNNs), 循环神经网络系列(RNNs), 生成对抗网络(GANs), 自编码器(Autoencoders) 等等。 因为反向传播算法的存在和各类数学优化器的发展, 使得第二代神经网络在各项任务上有着出色的表现。 Spiking Neural...
Spiking neural networkAutoencoderMobileNetSkin cancerMelanocytes are skin cells that give color to the skin and form melanin color pigments. The unbalanced division and proliferation of these cells result in skin cancer. The early diagnosis and proper treatment of skin cancer are so important. In ...
These range from Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM) Networks, Autoencoders up to Generative Adversarial Networks. Both discriminative and generative models are used in this context. However, due to the pronounced class imbalance and the associated lack of appropriate ...
Spike Neuron Model Variational Autoencoder Variational Recurent Neural Network Generative models in SNN 脉冲GAN(Kotariya和Ganguly 2021)使用两层SNN构造生成器和鉴别器来训练GAN;生成的图像的质量低。其中一个原因是,初次脉冲时间编码(time-to-first spike encoding)不能在脉冲序列的中间抓取整个图像。此外,由于SN...
[3] Joseph M Brader, Walter Senn, and Stefano Fusi. Learning real-world stimuli in a neural network with spike-driven synaptic dynamics. Neural computation, 19(11):2881-2912, 2007. [4] Kendra S Burbank. Mirrored stdp implements autoencoder learning in a network of spiking neurons. PLoS Com...
In order to examine the effect of the proposed methods on more general SNN inference, an autoencoder capable of image compression and decompression was trained, as this method is a neural network application in which the actual activation value itself is important. The encoding part of the auto...
(2022). Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks. Paper presented at the 10th International Conference on Learning Representations, virtually, 25–29 April 2022. Burbank, K. S. (2015). Mirrored stdp implements autoencoder learning in a network of...
CDNA-SNN: A New Spiking Neural Network for Pattern Classification Using Neuronal Assemblies [paper] Accurate and Efficient Event-Based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network [paper] [arxiv] [paper with code] Retina-Inspired Lightweight Spiking Convolutional Neural Network ...
However, regarding SNN there is a lack of information concerning the encoding method, the learning approach, the structure of the network and the neuron model, impeding comparison, reproducibility and replication. Show abstract Spiking autoencoder for nonlinear industrial process fault detection 2024, ...
Spiking neural networks (SNNs) have attracted significant research attention due to their inherent sparsity and event-driven processing capabilities. Recen