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Learning in Spiking Neural Networks by Reinforcement of Stochastic Synaptic Transmission

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…

Training spiking neural networks for reinforcement learning

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A review of learning in biologically plausible spiking neural networks

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…

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[CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural networks论文笔记

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（转）Understanding, generalisation, and transfer learning in deep neural networks

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This instability is a fundamental problem for gradient-based learning in deep neural networks. vanishing exploding gradient problem

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【论文阅读】Learning Dual Convolutional Neural Networks for Low-Level Vision

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[译]深度神经网络的多任务学习概览(An Overview of Multi-task Learning in Deep Neural Networks)

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Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient

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…