A novel spiking neural network of receptive field encoding with groups of neurons decision

  • Yong-qiang Ma
  • Zi-ru Wang
  • Si-yu Yu
  • Ba-dong Chen
  • Nan-ning Zheng
  • Peng-ju Ren


Human information processing depends mainly on billions of neurons which constitute a complex neural network, and the information is transmitted in the form of neural spikes. In this paper, we propose a spiking neural network (SNN), named MD-SNN, with three key features: (1) using receptive field to encode spike trains from images; (2) randomly selecting partial spikes as inputs for each neuron to approach the absolute refractory period of the neuron; (3) using groups of neurons to make decisions. We test MD-SNN on the MNIST data set of handwritten digits, and results demonstrate that: (1) Different sizes of receptive fields influence classification results significantly. (2) Considering the neuronal refractory period in the SNN model, increasing the number of neurons in the learning layer could greatly reduce the training time, effectively reduce the probability of over-fitting, and improve the accuracy by 8.77%. (3) Compared with other SNN methods, MD-SNN achieves a better classification; compared with the convolution neural network, MD-SNN maintains flip and rotation invariance (the accuracy can remain at 90.44% on the test set), and it is more suitable for small sample learning (the accuracy can reach 80.15% for 1000 training samples, which is 7.8 times that of CNN).


Tempotron Receptive field Difference of Gaussian (DoG) Flip invariance Rotation invariance 

CLC number



Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Al-Amri SS, Kalyankar NV, Khamitkar SD, 2010. Image segmentation by using edge detection. Int J Comput Sci Eng, 2(3):804–807.Google Scholar
  2. Berry MJII, Meister M, 1998. Refractoriness and neural precision. Proc Conf on Advances in Neural Information Processing Systems 10, p.110–116.Google Scholar
  3. Bi GQ, Poo MM, 2001. Synaptic modification by correlated activity: Hebb’s postulate revisited. Ann Rev Neurosci, 24(1):139–166. https://doi.org/10.1146/annurev.neuro.24.1.139CrossRefGoogle Scholar
  4. Brette R, Gerstner W, 2005. Adaptive exponential integrateand-fire model as an effective description of neuronal activity. J Neurophysiol, 94(5):3637–3642. https://doi.org/10.1152/jn.00686.2005CrossRefGoogle Scholar
  5. Burt P, Adelson E, 1983. The laplacian pyramid as a compact image code. IEEE Trans Commun, 31(4):532–540. https://doi.org/10.1109/TCOM.1983.1095851CrossRefGoogle Scholar
  6. Canny J, 1986. A computational approach to edge detection. IEEE Trans Patt Anal Mach Intell, 8(6):679–698. https://doi.org/10.1109/TPAMI.1986.4767851CrossRefGoogle Scholar
  7. Coates A, Ng A, Lee H, 2011. An analysis of single-layer networks in unsupervised feature learning. Proc 14th Int Conf on Artificial Intelligence and Statistics, p.215–223.Google Scholar
  8. Dasgupta S, Stevens CF, Navlakha S, 2017. A neural algorithm for a fundamental computing problem. Science, 358(6364):793–796. https://doi.org/10.1126/science.aam9868MathSciNetCrossRefGoogle Scholar
  9. Dora S, Suresh S, Sundararajan N, 2015a. A sequential learning algorithm for a spiking neural classifier. Appl Soft Comput, 36:255–268. https://doi.org/10.1016/j.asoc.2015.06.062CrossRefGoogle Scholar
  10. Dora S, Sundaram S, Sundararajan N, 2015b. A two stage learning algorithm for a growing-pruning spiking neural network for pattern classification problems. Int Joint Conf on Neural Networks, p.1–7. https://doi.org/10.1109/ijcnn.2015.7280592Google Scholar
  11. Dora S, Subramanian K, Suresh S, et al., 2016. Development of a self-regulating evolving spiking neural network for classification problem. Neurocomputing, 171:1216–1229. https://doi.org/10.1016/j.neucom.2015.07.086CrossRefGoogle Scholar
  12. Dora S, Suresh S, Sundararajan N, 2017. Online meta-neuron based learning algorithm for a spiking neural classifier. Inform Sci, 414:19–32. https://doi.org/10.1016/j.ins.2017.05.050CrossRefGoogle Scholar
  13. Fukushima K, 1980. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern, 36(4):193–202. https://doi.org/10.1007/bf00344251MathSciNetCrossRefMATHGoogle Scholar
  14. Gerstner W, Kistler W, 2002. Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge, UK. https://doi.org/10.1017/CBO9780511815706CrossRefMATHGoogle Scholar
  15. Ghosh Dastidar S, Adeli H, 2007. Improved spiking neural networks for eeg classification and epilepsy and seizure detection. Integr Comput Aided Eng, 14(3):187–212.Google Scholar
  16. Gilbert CD, Wiesel TN, 1992. Receptive field dynamics in adult primary visual cortex. Nature, 356(6365):150–152. https://doi.org/10.1038/356150a0CrossRefGoogle Scholar
  17. Gütig R, Sompolinsky H, 2006. The tempotron: a neuron that learns spike timing-based decisions. Nat Neurosci, 9(3):420–428. https://doi.org/10.1038/nn1643CrossRefGoogle Scholar
  18. Hannun AY, Case C, Casper J, et al., 2014. Deep speech: Scaling up end-to-end speech recognition. https://arxiv.org/abs/1412.5567Google Scholar
  19. He KM, Zhang XY, Ren SQ, et al., 2016. Deep residual learning for image recognition. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.770–778. https://doi.org/10.1109/CVPR.2016.90Google Scholar
  20. Hodgkin AL, Huxley AF, 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol, 117(4):500–544. https://doi.org/10.1113/jphysiol.1952.sp004764CrossRefGoogle Scholar
  21. Hubel DH, Wiesel TN, 1962. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol, 160(1):106–154. https://doi.org/10.1113/jphysiol.1962.sp006837CrossRefGoogle Scholar
  22. Hussain S, Liu SC, Basu A, 2014. Improved margin multiclass classification using dendritic neurons with morphological learning. IEEE Int Symp on Circuits and Systems, p.2640–2643. https://doi.org/10.1109/iscas.2014.6865715Google Scholar
  23. Izhikevich EM, 2001. Resonate-and-fire neurons. Neur Networks, 14(6-7):883–894. https://doi.org/10.1016/s0893-6080(01)00078-8CrossRefGoogle Scholar
  24. Izhikevich EM, 2003. Simple model of spiking neurons. IEEE Trans Neur Networks, 14(6):1569–1572. https://doi.org/10.1109/tnn.2003.820440MathSciNetCrossRefGoogle Scholar
  25. Izhikevich EM, 2004. Which model to use for cortical spiking neurons? IEEE Trans Neur Networks, 15(5):1063–1070. https://doi.org/10.1109/tnn.2004.832719CrossRefGoogle Scholar
  26. LeCun Y, Bengio Y, Hinton G, 2015. Deep learning. Nature, 521(7553):436–444. https://doi.org/10.1038/nature14539CrossRefGoogle Scholar
  27. Legenstein R, Naeger C, Maass W, 2006. What can a neuron learn with spike-timing-dependent plasticity? Neur Comput, 17(11):2337–2382. https://doi.org/10.1162/0899766054796888MathSciNetCrossRefMATHGoogle Scholar
  28. Ma YQ, Wu H, Zhu MJ, et al., 2017. Reconstruction of visual image from functional magnetic resonance imaging using spiking neuron model. IEEE Trans Cogn Dev Syst, in press. https://doi.org/10.1109/tcds.2017.2764948Google Scholar
  29. Maass W, 1997. Networks of spiking neurons: the third generation of neural network models. Neur Networks, 10(9):1659–1671. https://doi.org/10.1016/s0893-6080(97)00011-7CrossRefGoogle Scholar
  30. Masquelier T, Guyonneau R, Thorpe SJ, 2009. Competitive stdp-based spike pattern learning. Neur Comput, 21(5):1259–1276. https://doi.org/10.1162/neco.2008.06-08-804CrossRefMATHGoogle Scholar
  31. Merolla P, Arthur J, Akopyan F, et al., 2011. A digital neurosynaptic core using embedded crossbar memory with 45pj per spike in 45nm. IEEE Custom Integrated Circuits Conf, p.1–4. https://doi.org/10.1109/cicc.2011.6055294Google Scholar
  32. Ponulak F, Kasinski A, 2010. Supervised learning in spiking neural networks with resume: sequence learning, classification, and spike shifting. Neur Comput, 22(2):467–510. https://doi.org/10.1162/neco.2009.11-08-901MathSciNetCrossRefMATHGoogle Scholar
  33. Rodieck RW, 1965. Quantitative analysis of cat retinal ganglion cell response to visual stimuli. Vis Res, 5(11):583–601. https://doi.org/10.1016/0042-6989(65)90033-7CrossRefGoogle Scholar
  34. Schmidhuber J, 2015. Deep learning in neural networks: An overview. Neur networks, 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003CrossRefGoogle Scholar
  35. Sobel I, 2014. History and definition of the sobel operator. https://www.scribd.com/document/271811982/Historyand-Definition-of-Sobel-OperatorGoogle Scholar
  36. Tang H, Yu Q, Tan KC, 2012. Learning real-world stimuli by single-spike coding and tempotron rule. Int Joint Conf on Neural Networks, p.1–6. https://doi.org/10.1109/ijcnn.2012.6252369Google Scholar
  37. Tavanaei A, Maida AS, 2015. A minimal spiking neural network to rapidly train and classify handwritten digits in binary and 10-digit tasks. Int J Adv Res Artif Intell, 4(7):1–8. https://doi.org/10.14569/ijarai.2015.040701CrossRefGoogle Scholar
  38. Thorpe S, Delorme A, van Rullen R, 2001. Spike-based strategies for rapid processing. Neur Netw, 14(67):715–725. https://doi.org/10.1016/s0893-6080(01)00083-1CrossRefGoogle Scholar
  39. Victor JD, Purpura KP, 1996. Nature and precision of temporal coding in visual cortex: a metric-space analysis. J Neurophysiol, 76(2):1310–1326. https://doi.org/10.1152/jn.1996.76.2.1310CrossRefGoogle Scholar
  40. Wade JJ, Mcdaid LJ, Santos JA, et al., 2010. SWAT: a spiking neural network training algorithm for classification problems. IEEE Trans Neur Networks, 21(11):1817–1830. https://doi.org/10.1109/TNN.2010.2074212CrossRefGoogle Scholar
  41. Xie XR, Qu H, Yi Z, et al., 2017. Efficient training of supervised spiking neural network via accurate synapticefficiency adjustment method. IEEE Trans Neur Networks, 28(6):1411–1424. https://doi.org/10.1109/tnnls.2016.2541339Google Scholar
  42. Yeomans JS, 1979. The absolute refractory periods of selfstimulation neurons. Phys Behav, 22(5):911–919. https://doi.org/10.1016/0031-9384(79)90336-6CrossRefGoogle Scholar
  43. Yu Q, Tang HJ, Tan KC, et al., 2013. Rapid feedforward computation by temporal encoding and learning with spiking neurons. IEEE Trans Neur Networks, 24(10):1539–1552. https://doi.org/10.1109/TNNLS.2013.2245677Google Scholar
  44. Yu Q, Tang HJ, Tan KC, et al., 2014. A brain-inspired spiking neural network model with temporal encoding and learning. Neurocomputing, 138:3–13. https://doi.org/10.1016/j.neucom.2013.06.052CrossRefGoogle Scholar
  45. Zenke F, Ganguli S, 2017. Superspike: supervised learning in multi-layer spiking neural networks. https://arxiv.org/abs/1705.11146Google Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityXi’anChina

Personalised recommendations