郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Contents: ABSTRACT 1. Introduction 2. Biological background 2.1. Spiking neuron models 2.2. Synaptic plasticity 2.2.1. Unsupervised learning 2.2.2. Supervised learning 2.2.3. Reinforcement learning 2.2.4. Delay learning…
郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! 原文链接:https://arxiv.org/pdf/2005.05941.pdf Contents: Abstract Introduction 1 Reinforcement learning with a network of spiking agents 2 Related Work 2.0.1 Hedonism 2.0.2 Learning by reinforcement in spiking neural network…
ON THE EVOLUTION OF MACHINE LEARNING: FROM LINEAR MODELS TO NEURAL NETWORKS We recently interviewed Reza Zadeh (@Reza_Zadeh). Reza is a Consulting Professor in the Institute for Computational and Mathematical Engineering at Stanford University and a…
郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! arXiv:2001.01587v1 [cs.NE] 1 Jan 2020 Abstract 脉冲神经网络(SNN)被广泛应用于神经形态设备中,以模拟大脑功能.在这种背景下,SNN的安全性变得重要但缺乏深入的研究,这与深度学习的热潮不同.为此,我们针对SNN的对抗攻击,确认了与ANN攻击不同的几个挑战:i)当前的对抗攻击是基于SNN中以时空模式呈现的梯度信息,这在传统的学习算法中很难获得:ii)在梯度累积过程中,输入的连续梯度与二值脉…
The Neural Network is one of the most powerful learning algorithms (when a linear classifier doesn't work, this is what I usually turn to), and this week's videos explain the 'backprogagation' algorithm for training these models. In this week's progr…
郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! arXiv:2003.10399v2 [cs.CV] 23 Jul 2020 ECCV 2020 1 https://github.com/ssharmin/spikingNN-adversarial-attack Abstract 在最近对可信任的神经网络的探索中,我们提出了一个潜在的候选,即脉冲神经网络(SNN)之于对抗攻击的内在鲁棒性.在这项工作中,我们证明对CIFAR数据集上的深度VGG和ResNet结构,在基于梯度的攻击下,…
郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Summary 众所周知,化学突触传递是不可靠的过程,但是这种不可靠的函数仍然不清楚.在这里,我考虑这样一个假设,即大脑利用突触传递的随机性来进行学习,这类似于达尔文进化论中的基因突变.如果突触是“享乐主义的”,则可能发生这种情况,通过增加它们的囊泡释放或失败的概率来响应全局奖励信号,这取决于立即采取哪种动作.享乐主义突触通过计算对平均奖励梯度的随机近似来学习.它们与突触动态(例如短期促进和抑制)以及树突整合和动作电位生成的复杂性兼容…
Deep L-layer neural network 1 - General methodology As usual you will follow the Deep Learning methodology to build the model: 1). Initialize parameters / Define hyperparameters 2). Loop for num_iterations: a. Forward propagation b. Compute cost func…
郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Abstract 灵长类视觉系统激发了深度人工神经网络的发展,使计算机视觉领域发生了革命性的变化.然而,这些网络的能量效率比它们的生物学对应体要低得多,而且它们通常使用反向传播进行训练,这是非常需要数据的.为了解决这些限制,我们使用了深度卷积脉冲神经网络(DCSNN)和延迟编码方案.我们将最低层的脉冲时序依赖可塑性(STDP)和最高层的奖励调节STDP(R-STDP)结合起来训练.简而言之,在R-STDP中,正确(错误)决策导致STD…
Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets(解释口译) and understand your voice commands, it is likely that a neural network is helping to understand your speech; wh…