A novel learning approach in deep spiking neural networks with multi-objective optimization algorithms for automatic digit speech recognition Here, a new layered spiking neural network (SNN) learning framework is proposed using optimization algorithms for rapid and efficient pattern recognition a... M Ha...
Deep learningSpiking neural networkBiological plausibilityMachine learningPower-efficient architectureIn recent years, deep learning has revolutionized the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained, most ...
本文发现训练浅层的SNN models用soft reset获得更好的结果,但是深层的SNN models用hard reset效果更好。这个观点与2020年CVPR文章《RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network》相矛盾,但是后者用的是ANN2SNN的方法,可能机制不一样,只能跑...
深度SNN为使用新型基于事件的传感器、利用时间代码和局部片上学习提供了很好的机会,到目前为止,我们只是在实际应用中实现了这些优势的皮毛。 Keywords: neural networks, spiking neurons, neuromorphic engineering, event-based computing, deep learning, binary networks 1. INTRODUCTION 使用深度神经网络(DNN)进行训练和...
Keywords: neural networks, spiking neurons, neuromorphic engineering, event-based computing, deep learning, binary networks 1. INTRODUCTION 使用深度神经网络(DNN)进行训练和推理,通常称为深度学习(LeCun et al., 2015; Schmidhuber, 2015; Goodfellow et al., 2016),为人工智能(AI)的许多引人注目的成功案例...
Keywords:Spiking neural networks, Deep reinforcement learning, Energy-efficient continuous control 1 Introduction 具有连续高维观察和动作空间的移动机器人正越来越多地被部署来解决复杂的实际任务。鉴于其有限的主板能量资源,迫切需要设计节能解决方案来对这些自主机器人进行连续控制。基于策略梯度的深度强化学习(DRL)方法...
Norse expands PyTorch with primitives for bio-inspired neural components, bringing you two advantages: a modern and proven infrastructure based on PyTorch and deep learning-compatible spiking neural network components. Documentation: norse.github.io/norse/ 1. Getting started The fastest way to try ...
Directly-trained Spiking Neural Networks for Deep Reinforcement Learning: Energy efficient implementation of event-based obstacle avoidance on a neuromorph... Directly-trained Spiking Neural Networks for Deep Reinforcement Learning: Energy efficient implementation of event-based obstacle avoidance on a ...
In this paper we analyze in theory and simulation the learning dynamics of time-to-first-spike-networks and identify a specific instance of the vanishing-or-exploding gradient problem. While two choices of spiking neural network mappings solve this problem at initialization, only the one with a ...
A library for deep learning with Spiking Neural Networks (SNN)powered byGeNN, a GPU enhanced Neuronal Network simulation environment. Installation Follow the instructions inhttps://genn-team.github.io/genn/documentation/5/installation.htmlto install PyGeNN. ...