Firing patterns in delayed feedforward networks with STDP rules
详细信息 查看官网全文
- 英文论文题名：Firing patterns in delayed feedforward networks with STDP rules
- 论文作者：Yan-Qiu Che ; Ying-Mei Qin ; Jia Zhao ; Chun-xiao Han
- 英文论文作者：Yan-Qiu Che ; Ying-Mei Qin ; Jia Zhao ; Chun-xiao Han ; Tianjin Key Laboratory of Information Sensing & Intelligent Control ; Tianjin University of Technology and Education ; Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology ; Southwest University
- 年：2017
- 作者机构：Tianjin Key Laboratory of Information Sensing & Intelligent Control,Tianjin University of Technology and Education;Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology,Southwest University;
- 英文论文关键词：Feedforward network ; STDP rules ; Firing rate propagation
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：TP183
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：5
- 文件大小：319k
- 原文格式：D
- 会议级别：全国
摘要
We investigate the evolution of a delayed feedforward network model with plasticity. It is found that desynchronized and synchronized firing patterns both exist in the feedforward network(FFN) with different synaptic delays. Synaptic delays may play a significant role in bridging rate coding and time coding. Then we focus on the evolution of firing rate and synaptic weights in the FFN network based on the spike-timing dependent plasticity(STDP) rules. The firing rates can be reliably transmitted after the evolution based on STDP rules. The mean synaptic weights tend to be saturated after certain time of evolution. Furthermore,the firing information can be shifted by the changes of network connection and STDP rules through the FFN network.
We investigate the evolution of a delayed feedforward network model with plasticity. It is found that desynchronized and synchronized firing patterns both exist in the feedforward network(FFN) with different synaptic delays. Synaptic delays may play a significant role in bridging rate coding and time coding. Then we focus on the evolution of firing rate and synaptic weights in the FFN network based on the spike-timing dependent plasticity(STDP) rules. The firing rates can be reliably transmitted after the evolution based on STDP rules. The mean synaptic weights tend to be saturated after certain time of evolution. Furthermore,the firing information can be shifted by the changes of network connection and STDP rules through the FFN network.
We investigate the evolution of a delayed feedforward network model with plasticity. It is found that desynchronized and synchronized firing patterns both exist in the feedforward network(FFN) with different synaptic delays. Synaptic delays may play a significant role in bridging rate coding and time coding. Then we focus on the evolution of firing rate and synaptic weights in the FFN network based on the spike-timing dependent plasticity(STDP) rules. The firing rates can be reliably transmitted after the evolution based on STDP rules. The mean synaptic weights tend to be saturated after certain time of evolution. Furthermore,the firing information can be shifted by the changes of network connection and STDP rules through the FFN network.
引文
[1]A.Kumar,S.Rotter,A.Aertsen,Spiking activity propagation in neuronal networks:reconciling different perspectives on neural coding,Nat.Rev.Neurosci.,11,615-627,2010.
[2]J.Zhao,B.Deng,Y.M.Qin,C.Men,J.Wang,X.L.Wei,J.B.Sun.Weak electric fields detectability in a noisy neural network,Cogn Neurodyn,11(1):81-90,2017.
[3]Spikes:Exploring the Neural Code(Computational Neuroscience),The MIT Press,1999.
[4]A.D.Reyes,Synchrony-dependent propagation of firing rate in iteratively constructed networks in vitro,Nat.Neurosci.,6,593-599,2003.
[5]S.T.Wang,W.Wang,F.Liu,Propagation of firing rate in a feed-forward neuronal network,Phys.Rev.Lett.,96,018103,2006.
[6]M.Yi,L.J.Yang,Propagation of firing rate by synchronization and coherence of firing pattern in a feed-forward multilayer neural network,Phys.Rev.E,81,061924,2010.
[7]R.X.Han,J.Wang,H.T.Yu,B.Deng,X.L.Wei,Y.M.Qin,and H.X.Wang.Intrinsic excitability state of local neuronal population modulates signal propagation in feed-forward neural networks.Chaos 25,043108,2015.
[8]J.Wang,R.X.Han,X.L.Wei,Y.M.Qin,H.T.Yu,B.Deng,Weak signal detection and propagation in diluted feed forward neural network with recurrent excitation and inhibition,Int.J.Mod.Phys.B 30,1550253,2016.
[9]Gilson,M.,A.N.Burkitt,D.B.Grayden,D.A.Thomas and J.L.van Hemmen.Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks.II.Input selectivity-symmetry breaking.Biological Cybernetics,101(2),103-114,2009.
[10]Leibold,C.,R.Kempter and J.L.van Hemmen.How spiking neurons give rise to a temporal-feature map:From synaptic plasticity to axonal selection.Physical Review E,65(5),2002.
[11]Burkitt,A.N.,H.Meffin and D.B.Grayden.Spike-timingdependent plasticity:The relationship to rate-based learning for models with weight dynamics determined by a stable fixed point.Neural Computation,16(5),885-940,2004.
[12]Meffin,H.,J.Besson,A.N.Burkitt and D.B.Grayden.Learning the structure of correlated synaptic subgroups using stable and competitive spike-timing-dependent plasticity.Physical Review E,Vol.73,No.4,2006.
[13]Mehta,M.R.,A.K.Lee and M.A.Wilson.Role of experience and oscillations in transforming a rate code into a temporal code.Nature,417(6890),741-746,2002.
[14]Scarpetta,S.and M.Marinaro.A learning rule for place fields in a cortical model:Theta phase precession as a network effect.Hippocampus,15(7),979-989,2005.
[15]Froemke,R.C.,D.Debanne and G.-Q.Bi.Temporal modulation of spike-timing-dependent plasticity.Frontiers in synaptic neuroscience,2(19),2010.
[16]Froemke,R.C.and Y.Dan.Spike-timing-dependent synaptic modification induced by natural spike trains.Nature,416(6879),433-438,2002.
[17]S.Song,K.D.Miller,L.F.Abbott,Competitive Hebbian learning through spike-timing-dependent synaptic plasticity,Nat.Neurosci.,3,919-926,2000.
[18]C.-W.Shin,S.Kim,Self-organized criticality and scale-free properties in emergent functional neural networks,Phys.Rev.E,74,045101,2006.
[19]S.Kang,K.Kitano,T.Fukai,Self-organized two-state membrane potential transitions in a network of realistically modeled cortical neurons,Neural Networks,17,307-312,2004.
[20]N.Masuda,H.Kori,Formation of feedforward networks and frequency synchrony by spike-timing-dependent plasticity,Journal of Computational Neuroscience,22,327-345,2007.
[21]J.K.Jun,D.Z.Jin,Development of Neural Circuitry for Precise Temporal Sequences through Spontaneous Activity,Axon Remodeling,and Synaptic Plasticity,PLo S ONE,2,e723,2007.
[22]C.Zhou,J.Kurths,B.Hu,Array-Enhanced Coherence Resonance:Nontrivial Effects of Heterogeneity and Spatial Independence of Noise,Phys.Rev.Lett.87,098101,2001.
[23]S.Luccioli and A.Politi,Irregular Collective Behavior of Heterogeneous Neural Networks,Phys.Rev.Lett.105,158104,2010.
[24]M.Li,H.Greenside,Stable propagation of a burst through a one-dimensional homogeneous excitatory chain model of songbird nucleus HVC,Phys.Rev.E,74,011918,2006.
[25]C.Rocsoreanu,A.Georgescu,and N.Giurgiteanu The Fitz Hugh-Nagumo Model:Bifurcation and Dynamics.Kluwer Academic Publishers,Boston,2000.
[2]J.Zhao,B.Deng,Y.M.Qin,C.Men,J.Wang,X.L.Wei,J.B.Sun.Weak electric fields detectability in a noisy neural network,Cogn Neurodyn,11(1):81-90,2017.
[3]Spikes:Exploring the Neural Code(Computational Neuroscience),The MIT Press,1999.
[4]A.D.Reyes,Synchrony-dependent propagation of firing rate in iteratively constructed networks in vitro,Nat.Neurosci.,6,593-599,2003.
[5]S.T.Wang,W.Wang,F.Liu,Propagation of firing rate in a feed-forward neuronal network,Phys.Rev.Lett.,96,018103,2006.
[6]M.Yi,L.J.Yang,Propagation of firing rate by synchronization and coherence of firing pattern in a feed-forward multilayer neural network,Phys.Rev.E,81,061924,2010.
[7]R.X.Han,J.Wang,H.T.Yu,B.Deng,X.L.Wei,Y.M.Qin,and H.X.Wang.Intrinsic excitability state of local neuronal population modulates signal propagation in feed-forward neural networks.Chaos 25,043108,2015.
[8]J.Wang,R.X.Han,X.L.Wei,Y.M.Qin,H.T.Yu,B.Deng,Weak signal detection and propagation in diluted feed forward neural network with recurrent excitation and inhibition,Int.J.Mod.Phys.B 30,1550253,2016.
[9]Gilson,M.,A.N.Burkitt,D.B.Grayden,D.A.Thomas and J.L.van Hemmen.Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks.II.Input selectivity-symmetry breaking.Biological Cybernetics,101(2),103-114,2009.
[10]Leibold,C.,R.Kempter and J.L.van Hemmen.How spiking neurons give rise to a temporal-feature map:From synaptic plasticity to axonal selection.Physical Review E,65(5),2002.
[11]Burkitt,A.N.,H.Meffin and D.B.Grayden.Spike-timingdependent plasticity:The relationship to rate-based learning for models with weight dynamics determined by a stable fixed point.Neural Computation,16(5),885-940,2004.
[12]Meffin,H.,J.Besson,A.N.Burkitt and D.B.Grayden.Learning the structure of correlated synaptic subgroups using stable and competitive spike-timing-dependent plasticity.Physical Review E,Vol.73,No.4,2006.
[13]Mehta,M.R.,A.K.Lee and M.A.Wilson.Role of experience and oscillations in transforming a rate code into a temporal code.Nature,417(6890),741-746,2002.
[14]Scarpetta,S.and M.Marinaro.A learning rule for place fields in a cortical model:Theta phase precession as a network effect.Hippocampus,15(7),979-989,2005.
[15]Froemke,R.C.,D.Debanne and G.-Q.Bi.Temporal modulation of spike-timing-dependent plasticity.Frontiers in synaptic neuroscience,2(19),2010.
[16]Froemke,R.C.and Y.Dan.Spike-timing-dependent synaptic modification induced by natural spike trains.Nature,416(6879),433-438,2002.
[17]S.Song,K.D.Miller,L.F.Abbott,Competitive Hebbian learning through spike-timing-dependent synaptic plasticity,Nat.Neurosci.,3,919-926,2000.
[18]C.-W.Shin,S.Kim,Self-organized criticality and scale-free properties in emergent functional neural networks,Phys.Rev.E,74,045101,2006.
[19]S.Kang,K.Kitano,T.Fukai,Self-organized two-state membrane potential transitions in a network of realistically modeled cortical neurons,Neural Networks,17,307-312,2004.
[20]N.Masuda,H.Kori,Formation of feedforward networks and frequency synchrony by spike-timing-dependent plasticity,Journal of Computational Neuroscience,22,327-345,2007.
[21]J.K.Jun,D.Z.Jin,Development of Neural Circuitry for Precise Temporal Sequences through Spontaneous Activity,Axon Remodeling,and Synaptic Plasticity,PLo S ONE,2,e723,2007.
[22]C.Zhou,J.Kurths,B.Hu,Array-Enhanced Coherence Resonance:Nontrivial Effects of Heterogeneity and Spatial Independence of Noise,Phys.Rev.Lett.87,098101,2001.
[23]S.Luccioli and A.Politi,Irregular Collective Behavior of Heterogeneous Neural Networks,Phys.Rev.Lett.105,158104,2010.
[24]M.Li,H.Greenside,Stable propagation of a burst through a one-dimensional homogeneous excitatory chain model of songbird nucleus HVC,Phys.Rev.E,74,011918,2006.
[25]C.Rocsoreanu,A.Georgescu,and N.Giurgiteanu The Fitz Hugh-Nagumo Model:Bifurcation and Dynamics.Kluwer Academic Publishers,Boston,2000.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |