不同于经典人工神经网络(Artificial neural network, ANN),SNN下的信息被编码于各个神经元产生的尖峰列(spike train)中。此外,SNN下各个神经元维护着与时间相关的状态变量。 膜电位(membrane)为SNN下的神经元所维护的一个重要状态。为模拟生物神经元膜电位的变化,当前存在不同的模型。这些模型在相关工作中也被称为...
Installing PySNN requires a Python version of 3.6 or higher, Python 2 is not supported. It also requires PyTorch to be of version 1.2 or higher. Intention is to mirror most of the structure of PyTorch framework. As an example, the followig piece of code shows how much a Spiking Neural ...
Pure python implementation of SNN . Contribute to Shikhargupta/Spiking-Neural-Network development by creating an account on GitHub.
Keywords:GPU-computing, spiking Network, PyTorch, machine learning, python (programming language), reinforcement learning (RL) 1. INTRODUCTION 深度学习模型在计算机视觉,自然语言处理和其他领域的最新成功(LeCun et al., 2015)导致机器学习软件包的激增(Jia et al., 2014; Abadi et al., 2015; Chen et ...
资源大小2.59MB,文件格式.rar,国外开发的一个脉冲神经网络工具包,matlab和python均可使用,适合类脑和spiking神经网络研究使用
3 Vulnerability of Spiking Neural Networks 3.1 Spiking Neural Network under Attack 尽管在某些情况下,SNN比ANN更鲁棒,但当应用有效的对抗攻击方法时,大多数SNN仍然很脆弱。先前的工作已经通过实验证明,梯度攻击方法(如FGSM)可以应用于SNN [Sharmin et al., 2019, 2020]。然而,由于脉冲神经元的不可微分特性,通过...
简介 SpikingJelly 是一个基于 PyTorch,使用脉冲神经网络 (Spiking Network, SNN) 进行深度学习的框架 暂无标签 https://www.oschina.net/p/spikingjelly Python等 3 种语言 保存更改 发行版 暂无发行版 贡献者(71) 全部 近期动态 3年多前创建了仓库
The brain is the perfect place to look for inspiration to develop more efficient neural networks. One of the main differences with modern deep learning is that the brain encodes information in spikes rather than continuous activations. snnTorch is a Python package for performing gradient-based learn...
Goodman et al., Brian: a simulator for spiking neural networks in Python. Frontiers in Neuroinformatics. Nov. 2008, pp. 1-10, vol. 2, Article 5. Gorchetchnikov et al., NineML: declarative, mathematically-explicit descriptions of spiking neuronal networks, Frontiers in Neuroinformatics. Confer...
Disclosed herein are system, method, and computer program product embodiments for an improved spiking neural network (SNN) configured to learn and perform unsupervised extraction of