脉冲神经网络的应用与优劣势 从理论上讲,SNNs可用于与标准ANNs相同的应用。目前,SNNs主要的应用领域还是在脑科学与认知科学中,用于了解生物动物的中枢神经系统,如昆虫在陌生环境中寻找食物。由于SNNs的相似性,它们可以被用来研究生物大脑网络的运作:从一个关于真实神经回路的拓扑结构和功能的假设开始,该回路的记录可以与...
脉冲神经网络Spiking neuralnetworks(SNNs)是第三代神经网络模型,其模拟神经元更加接近实际,除此之外,把时间信息的影响也考虑当中。思路是这种,动态神经网络中的神经元不是在每一次迭代传播中都被激活(而在典型的多层感知机网络中却是),而是在它的膜电位达到某一个特定值才被激活。当一个神经元被激活,它会产生一个...
Current artificial neural networks are far from the human brain in structure (most are only based on the single neural circuit, such as feedforward or recurrent structures) and computation units (based on real values). Spiking Neural Networks (SNNs) are considered the third generation of ...
脉冲神经网络(SNNs)由于其事件驱动和低功耗的特点,作为第三代神经网络受到了广泛的关注。然而,SNN很难训练,主要是由于它们复杂的神经元动力学和不可微的脉冲操作。此外,它们的应用局限于相对简单的任务,如图像分类。在这项研究中,我们研究了SNNs在一个更具挑战性的回归问题(即目标检测)中的性能退化原因。通过深入分...
{Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the ...
英文摘要:Spiking neural networks (SNNs) offer a promising energy-efficient alternativeto artificial neural networks, due to their event-driven spiking computation.However, state-of-the-art deep SNNs (including Spikformer and SEW ResNet)suffer from non-spike computati...
SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks [paper] [code] Are Conventional SNNs Really Efficient? A Perspective from Network Quantization [paper] SFOD: Spiking Fusion Object Detector [paper] [code] ECCV-2024 Asynchronous Bioplausible Neuron for Spiking Neur...
Limitation of leaky-ReLU implementation in SNNs ReLU是最常用的激活函数,保留正值而去掉所有的负值,目前的DNN-to-SNN方法都专注于IF神经元与ReLU间的转换,忽略了激活函数中的负值,而在Tiny-YOLO中,负值激活占了51%。leaky-ReLU是目前最常用的激活,通过leakage项来保留负值f(x)=αxf(x)=αx,αα一般为...
Limitation of leaky-ReLU implementation in SNNs ReLU是最常用的激活函数,保留正值而去掉所有的负值,目前的DNN-to-SNN方法都专注于IF神经元与ReLU间的转换,忽略了激活函数中的负值,而在Tiny-YOLO中,负值激活占了51%。leaky-ReLU是目前最常用的激活,通过leakage项来保留负值$f(x)=\alpha x$,$\alpha$一般为...
Limitation of leaky-ReLU implementation in SNNs ReLU是最常用的激活函数,保留正值而去掉所有的负值,目前的DNN-to-SNN方法都专注于IF神经元与ReLU间的转换,忽略了激活函数中的负值,而在Tiny-YOLO中,负值激活占了51%。leaky-ReLU是目前最常用的激活,通过leakage项来保留负值f(x)=\alpha x,\alpha一般为0.01,但...