Correction: Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network.
作者信息
Cone Ian, Shouval Harel Z
出版信息
Elife. 2023 Mar 13;12:e87507. doi: 10.7554/eLife.87507.
Abstract
摘要
相似文献
1
Correction: Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network.更正：在模块化脉冲神经网络中通过生物物理现实学习规则学习精确的时空序列。
Elife. 2023 Mar 13;12:e87507. doi: 10.7554/eLife.87507.
2
Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network.通过模块化、脉冲神经网络中的生物物理现实学习规则学习精确的时空序列。
Elife. 2021 Mar 18;10:e63751. doi: 10.7554/eLife.63751.
3
Learning spatiotemporal signals using a recurrent spiking network that discretizes time.利用对时间进行离散化的循环尖峰网络来学习时空信号。
PLoS Comput Biol. 2020 Jan 21;16(1):e1007606. doi: 10.1371/journal.pcbi.1007606. eCollection 2020 Jan.
4
Learning sensory maps with real-world stimuli in real time using a biophysically realistic learning rule.
IEEE Trans Neural Netw. 2002;13(3):619-32. doi: 10.1109/TNN.2002.1000128.
5
Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons.通过脉冲神经元的层次网络学习具有多个时间尺度的组合序列。
PLoS Comput Biol. 2021 Mar 25;17(3):e1008866. doi: 10.1371/journal.pcbi.1008866. eCollection 2021 Mar.
6
Learning recurrent dynamics in spiking networks.学习尖峰网络中的循环动力学。
Elife. 2018 Sep 20;7:e37124. doi: 10.7554/eLife.37124.
7
Partial coupling delay induced multiple spatiotemporal orders in a modular neuronal network.部分耦合延迟在模块化神经网络中诱导出多种时空秩序。
PLoS One. 2017 Jun 1;12(6):e0177918. doi: 10.1371/journal.pone.0177918. eCollection 2017.
8
Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network.基于脉冲神经网络的可解释脑电模式的深度学习：动态时空聚类和规则。
Sensors (Basel). 2021 Jul 19;21(14):4900. doi: 10.3390/s21144900.
9
Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.镜像脉冲时间依赖可塑性在脉冲神经元网络中实现自动编码器学习。
PLoS Comput Biol. 2015 Dec 3;11(12):e1004566. doi: 10.1371/journal.pcbi.1004566. eCollection 2015 Dec.
10
Evolving interpretable plasticity for spiking networks.用于脉冲神经网络的不断发展的可解释可塑性。
Elife. 2021 Oct 28;10:e66273. doi: 10.7554/eLife.66273.
引用本文的文献
1
Toward reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning.迈向可重复的序列学习模型：具有基于奖励学习的模块化脉冲神经网络的复制与分析
Front Integr Neurosci. 2023 Jun 15;17:935177. doi: 10.3389/fnint.2023.935177. eCollection 2023.
本文引用的文献
1
Toward reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning.迈向可重复的序列学习模型：具有基于奖励学习的模块化脉冲神经网络的复制与分析
Front Integr Neurosci. 2023 Jun 15;17:935177. doi: 10.3389/fnint.2023.935177. eCollection 2023.
Zotero 插件