全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
电子学报  2015 

脉冲神经网络的监督学习算法研究综述

DOI: 10.3969/j.issn.0372-2112.2015.03.024, PP. 577-586

Keywords: 脉冲神经网络,监督学习,反向传播,突触可塑性,卷积

Full-Text   Cite this paper   Add to My Lib

Abstract:

脉冲神经网络是进行复杂时空信息处理的有效工具,但由于其内在的不连续和非线性机制,构建高效的脉冲神经网络监督学习算法非常困难,同时也是该研究领域的重要问题.本文介绍了脉冲神经网络监督学习算法的基本框架,以及性能评价原则,包括脉冲序列学习能力、离线与在线处理性能、学习规则的局部特性和对神经网络结构的适用性.此外,对脉冲神经网络监督学习算法的梯度下降学习规则、突触可塑性学习规则和脉冲序列卷积学习规则进行了详细的讨论,通过对比分析指出现有算法存在的优缺点,并展望了该领域未来的研究方向.

References

[1]  Kasabov N K.Neu Cube:A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data[J].Neural Networks, 2014, 52:62-76.
[2]  Oniz Y, Kaynak O.Variable-structure-systems based approach for online learning of spiking neural networks and its experimental evaluation[J].Journal of the Franklin Institute, 2014, 351(6):3269-3285.
[3]  Rumelhart D E, Hinton G E, Williams R J.Learning representations by back-propagating errors[J].Nature, 1986, 323(9):533-536.
[4]  Bohte S M, Kok J N, La Poutré J A.Error-backpropagation in temporally encoded networks of spiking neurons[J].Neurocomputing, 2002, 48(1-4):17-37.
[5]  Gerstner W, Kistler W M.Spiking Neuron Models:Single Neurons, Populations, Plasticity[M].Cambridge:Cambridge University Press, 2002.
[6]  Xin J, Embrechts M J.Supervised learning with spiking neuron networks[A].Proceedings of the International Joint Conference on Neural Networks[C].Washington DC:IEEE, 2001.1772-1777.
[7]  Schrauwen B, Van Campenhout J.Extending SpikeProp[A].Proceedings of the International Joint Conference on Neural Networks[C].Budapest, Hungary:IEEE, 2004.471-475.
[8]  Mc Kennoch S, Liu D, Bushnell L G.Fast modifications of the SpikeProp algorithm[A].Proceedings of the International Joint Conference on Neural Networks[C].Vancouver, Canada:IEEE, 2006.3970-3977.
[9]  Mc Kennoch S, Voegtlin T, Bushnell L.Spike-timing error backpropagation in theta neuron networks[J].Neural Computation, 2009, 21(1):9-45.
[10]  Fang H, Luo J, Wang F.Fast learning in spiking neural networks by learning rate adaptation[J].Chinese Journal of Chemical Engineering, 2012, 20(6):1219-1224.
[11]  Yang W, Yang J, Wu W.A modified spiking neuron that involves derivative of the state function at firing time[J].Neural Processing Letters, 2012, 36(2):135-144.
[12]  Booij O, Nguyen T H.A gradient descent rule for spiking neurons emitting multiple spikes[J].Information Processing Letters, 2005, 95(6):552-558.
[13]  Booij O.Temporalpattern Classification Using Spiking Neural Networks[D].Amsterdam:University of Amsterdam, 2004.
[14]  方慧娟, 王永骥.多脉冲发放的Spiking神经网络[J].应用科学学报, 2008, 26(6):638-644. Fang Hui-juan, Wang Yong-ji.Spiking neural networks with neurons firing multiple spikes[J].Journal of Applied Sciences, 2008, 26(6):638-644.(in Chinese)
[15]  Tiňo P, Mills A J S.Learning beyond finite memory in recurrent networks of spiking neurons[J].Neural Computation, 2006, 18(3):591-613.
[16]  Gütig R, Sompolinsky H.The Tempotron:A neuron that learns spike timing-based decisions[J].Nature Neuroscience, 2006, 9(3):420-428.
[17]  Florian R V.The Chronotron:A neuron that learns to fire temporally precise spike patterns[J].PLoS One, 2012, 7(8):e40233.
[18]  Victor J D, Purpura K P.Metric-space analysis of spike trains:Theory, algorithms and application[J].Network:Computation in Neural Systems, 1997, 8(2):127-164.
[19]  Xu Y, Zeng X, Zhong S.A new supervised learning algorithm for spiking neurons[J].Neural Computation, 2013, 25(6):1472-1511.
[20]  Le Mouel C, Harris K D, Yger P.Supervised learning with decision margins in pools of spiking neurons[J].Journal of Computational Neuroscience, 2014, 37(2):333-344
[21]  Haykin S S.Neural Networks and Learning Machines[M].Upper Saddle River:Pearson Education, 2009.
[22]  Izhikevich E M.Which model to use for cortical spiking neurons?[J].IEEE Transactions on Neural Networks, 2004, 15(5):1063-1070.
[23]  Bohte S M.The evidence for neural information processing with precise spike-times:A survey[J].Natural Computing, 2004, 3(2):195-206.
[24]  Ghosh-Dastidar S, Adeli H.Spiking neural networks[J].International Journal of Neural Systems, 2009, 19(4):295-308.
[25]  Knudsen E I.Supervised learning in the brain[J].Journal of Neuroscience, 1994, 14(7):3985-3997.
[26]  Kasiński A, Ponulak F.Comparison of supervised learning methods for spike time coding in spiking neural networks[J].International Journal of Applied Mathematics and Computer Science, 2006, 16(1):101-113.
[27]  Quiroga R Q, Panzeri S.Principles of Neural Coding[M].Boca Raton, FL:CRC Press, 2013.
[28]  蔺想红, 张田文.分段线性脉冲神经元模型的动力学特性分析[J].电子学报, 2009, 37(6):1270-1276. Lin Xiang-hong, Zhang Tian-wen.Dynamical properties of piecewise linear spiking neuron model[J].Acta Electronica Sinica, 2009, 37(6):1270-1276.(in Chinese)
[29]  Brette R, Rudolph M, Carnevale T, et al.Simulation of networks of spiking neurons:A review of tools and strategies[J].Journal of Computational Neuroscience, 2007, 23(3):349-398.
[30]  Naud R, Gerhard F, Mensi S, et al.Improved similarity measures for small sets of spike trains[J].Neural Computation, 2011, 23(12):3016-3069.
[31]  Wang J, Belatreche A, Maguire L, et al.Online versus offline learning for spiking neural networks:A review and new strategies[A].Proceedings of the 9th International Conference on Cybernetic Intelligent Systems[C].London, UK:IEEE, 2010.1-6.
[32]  Ghosh-Dastidar S, Adeli H.A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection[J].Neural Networks, 2009, 22(10):1419-1431.
[33]  Xu Y, Zeng X, Han L, et al.A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks[J].Neural Networks, 2013, 43:99-113.
[34]  巩祖正.脉冲神经网络的多脉冲定时误差反向传播算法研究[D].甘肃兰州:西北师范大学, 2013. Gong Zu-zheng.Multi-spike Timing Error Back Propagation Algorithm in Spiking Neural Networks[D].Lanzhou, Gansu:Northwest Normal University, 2013.(in Chinese)
[35]  Hebb D O.The Organization of Behavior:A Neuropsychological Theory[M].New York:Wiley, 1949.
[36]  Caporale N, Dan Y.Spike timing-dependent plasticity:A Hebbian learning rule[J].Annual Review of Neuroscience, 2008, 31(1):25-46.
[37]  Ruf B, Schmitt M.Learning temporally encoded patterns in networks of spiking neurons[J].Neural Processing Letters, 1997, 5(1):9-18.
[38]  Legenstein R, Naeger C, Maass W.What can a neuron learn with spike-timing-dependent plasticity?[J].Neural Computation, 2005, 17(11):2337-2382.
[39]  Ponulak F, Kasinski A.Supervised learning in spiking neural networks with ReSuMe:Sequence learning, classification, and spike shifting[J].Neural Computation, 2010, 22(2):467-510.
[40]  Ponulak F.Analysis of the ReSuMe learning process for spiking neural networks[J].International Journal of Applied Mathematics and Computer Science, 2008, 18(2):117-127.
[41]  Glackin C, Maguire L, Mc Daid L, et al.Receptive field optimisation and supervision of a fuzzy spiking neural network[J].Neural Networks, 2011, 24(3):247-256.
[42]  Hu J, Tang H, Tan K C, et al.A spike-timing-based integrated model for pattern recognition[J].Neural Computation, 2013, 25(2):450-472.
[43]  Sporea I, Grüning A.Supervised learning in multilayer spiking neural networks[J].Neural Computation, 2013, 25(2):473-509.
[44]  Wade J J, Mc Daid L J, Santos J A, et al.SWAT:A spiking neural network training algorithm for classification problems[J].IEEE Transactions on Neural Networks, 2010, 21(11):1817-1830.
[45]  Gardner B, Grüning A.Learning temporally precise spiking patterns through reward modulated spike-timing-dependent plasticity[A].Proceedings of the 23rd International Conference on Artificial Neural Networks[C].Sofia, Bulgaria:Springer Berlin Heidelberg, 2013.256-263.
[46]  Paugam-Moisy H, Martinez R, Bengio S.Delay learning and polychronization for reservoir computing[J].Neurocomputing, 2008, 71(7):1143-1158.
[47]  Pfister J P, Toyoizumi T, Barber D, et al.Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning[J].Neural Computation, 2006, 18(6):1318-1348.
[48]  Brea J, Senn W, Pfister J P.Matching recall and storage in sequence learning with spiking neural networks[J].The Journal of Neuroscience, 2013, 33(23):9565-9575.
[49]  Carnell A, Richardson D.Linear algebra for times series of spikes[A].Proceedings of the 13th European Symposium on Artificial Neural Networks[C].Evere, Belgium:d-side, 2005.363-368.
[50]  Mohemmed A, Schliebs S.SPAN:Spike pattern association neuron for learning spatio-temporal spike patterns[J].International Journal of Neural Systems, 2012, 22(4):1250012.
[51]  Mohemmed A, Schliebs S, Matsuda S, Kasabov N.Training spiking neural networks to associate spatio-temporal input-output spike patterns[J].Neurocomputing, 2013, 107:3-10.
[52]  Yu Q, Tang H, Tan K C, Li H.Precise-spike-driven synaptic plasticity:Learning hetero-association of spatiotemporal spike patterns[J].PLoS One, 2013, 8(11):e78318.
[53]  Yu Q, Tang H, Tan K C, Yu H.A brain-inspired spiking neural network model with temporal encoding and learning[J].Neurocomputing, 2014, 138:3-13. [LL]
[54]  Mohemmed A, Kasabov N.Incremental learning algorithm for spatio-temporal spike pattern classification[A].Proceedings of the International Joint Conference on Neural Networks[C].Brisbane, QLD:IEEE, 2012.1-6.
[55]  Li C, Lu J, Wu C, et al.Bidirectional modification of presynaptic neuronal excitability accompanying spike timing-dependent synaptic plasticity[J].Neuron, 2004, 41(2):257-268.
[56]  Wu W, Srivastava A.An information-geometric framework for statistical inferences in the neural spike train space[J].Journal of Computational Neuroscience, 2011, 31(3):725-748.
[57]  Park I M, Seth S, Paiva A, et al.Kernel methods on spike train space for neuroscience:A tutorial[J].IEEE Signal Processing Magazine, 2013, 30(4):149-160.
[58]  Selvaratnam K, Kuroe Y, Mori T.Learning methods of recurrent spiking neural networks-Transient and oscillatory spike trains[J].Transactions of the Institute of Systems, Control and Information Engineers, 2000, 44(3):95-104.
[59]  Kuroe Y, Ueyama T.Learning methods of recurrent spiking neural networks based on adjoint equations approach[A].Proceedings of the International Joint Conference on Neural Networks[C].Barcelona, Spain:IEEE, 2010.1-8.
[60]  Rostro-Gonzalez H, Cessac B, Viéville T.Parameter estimation in spiking neural networks:A reverse-engineering approach[J].Journal of Neural Engineering, 2012, 9(2):026024.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133