In this study, we proposed motorSRNN, a recurrent SNN topologically inspired by the primate motor neural circuit. Employed to decode CST from the primary motor cortex of two monkeys performing 4-direction reaching tasks, the motorSRNN achieved average classification accuracies of 89.44 % and 79.87...
Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware. In Proc. ICRC (IEEE, 2016). Masquelier, T. & Thorpe, S. J. Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Comput. Biol. 3, e31 (2007). ...
尽管如此,在这些任务中,SNN与当前的深度学习解决方案之间存在显著性能差距。 3 Spiking Recurrent Neural Networks 在此,我们关注由一个或多个循环层组成的SNN,即脉冲循环神经网络(SRNN),如图1a所示。在这些网络中,我们使用两种类型的脉冲神经元之一:LIF神经元(LIF SRNN)和自适应脉冲神经元(Adaptive SRNN)。脉冲神经...
中文翻译版:Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks - 穷酸秀才大草包 - 博客园 (cnblogs.com) 针对sequential and streaming tasks,本文提出了一种自适应脉冲递归神经网络(SRNN),把标准的自适应多时间尺度的脉冲神经元建模为self-recurrent的神经单元,利用代理梯度...
SNN_文献阅读_Effective and Efficient Computation with Multiple-timescaleSpiking Recurrent Neural Networks Adaptive SRNN 基于多时间尺度脉冲循环神经网络的高效计算(SRNN) 中心思想: 使用替代梯度进行训练,克服SNN中梯度不连续的问题。 在PyTorch中直接使用BPTT进行训练。 结构 本文讨论由一个或者多个递归层组成的SNN—...
Article Open access 04 August 2020 A solution to the learning dilemma for recurrent networks of spiking neurons Article Open access 17 July 2020 High-performance deep spiking neural networks with 0.3 spikes per neuron Article Open access 09 August 2024 Introduction...
递归神经网络(Recurrent Neural Networks, RNNs)为大脑实现Gibbs采样提供了一个可能的结构基础。以下是详细说明递归神经网络如何在实现Gibbs采样中发挥作用的机制: 1. 递归神经网络的基本结构 递归神经网络是一种具有循环连接的神经网络,允许信息在网络中回流。每个神经元的状态不仅依赖于当前输入,还依赖于之前的状态。这...
Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneityLeaky integrate-and-fireRecurrent E/I networkIntrinsic heterogeneityNetwork heterogeneityDimension reductionHeterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a...
Noisy recurrent neural networks Advances in Neural Information Processing Systems, NeurIPS (2021), 10.48550/arXiv.2102.04877 Google Scholar 26 G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, R.R. Salakhutdinov Improving neural networks by preventing co-adaptation of feature detectors Prepr...
Wang, G., Sun, Y., Cheng, S., & Song, S. (2023). Evolving Connectivity for Recurrent Spiking Neural Networks.Paper presented at the 37th Conference on Neural Information Processing Systems, New Orleans, Louisiana, 10–16 December 2023. ...