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Using patterns of firing neurons in spiking neural networks for learning and early recognition of spatio-temporal patterns

  • Computational Intelligence for Vision and Robotics
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Abstract

In this paper, we propose a novel unsupervised learning approach for spatio-temporal pattern classification. We use a spike timing neural network with axonal conductance delays to learn the structure of spatio-temporal patterns from a small set of training samples and then use this network for classifying unseen patterns. The method also enables early classification of patterns, before they are completely observed. To transform a spatio-temporal pattern into a suitable form for the spiking network, we create a mapping process that transforms it into a spike train that can be used to train the network through spike-timing-dependent plasticity. Based on the trained network, we build models of the training samples as strings of characters in which each character represents a set of neurons that fire at a particular time step, in response to the pattern. For classification, we compute the longest common subsequence between the model strings corresponding to the input and training samples and choose the class of the training sample with highest similarity. This method is evaluated on a handwritten digits dataset with spatio-temporal information. We show that this method is robustly detecting the correct class early on. Comparison with one unsupervised and eight other supervised approaches shows that our proposed approach could compete or has better performance rather than all of them. Further analysis show that even for misclassified samples, our system can detect the correct class among the top three class labels.

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References

  1. Beyeler M, Dutt ND, Krichmar JL (2013) Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule. Neural Netw 48:109–124

    Article  Google Scholar 

  2. Bouckaert RR (2004) Bayesian networ k classifiers in WEKA. Department of Computer Science, University of Waikato, Hamilton

    Google Scholar 

  3. Calinon S, Billard AG (2007) What is the teachers role in robot programming by demonstration?: Toward benchmarks for improved learning. Interact Stud 8(3):441–464

    Article  Google Scholar 

  4. Calinon S, Guenter F, Billard A (2007) On learning, representing, and generalizing a task in a humanoid robot. IEEE Trans Syst Man Cybern Part B Cybern 37(2):286–298

    Article  Google Scholar 

  5. Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

    Google Scholar 

  6. Duda RO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New York

    MATH  Google Scholar 

  7. Gonzales RC, Woods RE (2002) Digital image processing, vol 6. Prentice Hall, New Jersey, p 681

  8. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10–18

    Article  Google Scholar 

  9. Heermann PD, Khazenie N (1992) Classification of multispectral remote sensing data using a back-propagation neural network. IEEE Trans Geosci Remote Sens 30(1):81–88

    Article  Google Scholar 

  10. Hossain M, Rekabdar B, Louis SJ, Dascalu S (2015) Forecasting the weather of Nevada: a deep learning approach. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp 1–6. IEEE

  11. Isard M, Blake A (1996) Contour tracking by stochastic propagation of conditional density. In: Computer Vision ECCV’96, pp 343–356. Springer

  12. Izhikevich EM (2006) Polychronization: computation with spikes. Neural Comput 18(2):245–282

    Article  MathSciNet  MATH  Google Scholar 

  13. Jaccard P (1912) The distribution of the flora in the alpine zone. 1. New Phytol 11(2):37–50

    Article  Google Scholar 

  14. Kawanami K, Fujimoto N (2012) GPU accelerated computation of the longest common subsequence. In: Keller R, Kramer D, Weiss J-P (eds) Facing the multicore-challenge II. Springer, Heidelberg, pp 84–95

    Chapter  Google Scholar 

  15. Keren D (2003) Recognizing image style and activities in video using local features and naive Bayes. Pattern Recognit Lett 24(16):2913–2922

    Article  Google Scholar 

  16. Kwok C, Fox D, Meila M (2004) Real-time particle filters. Proc IEEE 92(3):469–484

    Article  Google Scholar 

  17. Le QV, Zou WY, Yeung SY, Ng AY (2011) Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3361–3368. IEEE

  18. Le Cun BB, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1990) Handwritten digit recognition with a back-propagation network. In: Advances in neural information processing systems. Citeseer

  19. Le Cun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  20. LeCun Y, Jackel L, Bottou L, Brunot A, Cortes C, Denker J, Drucker H, Guyon I, Muller U, Sackinger E et al (1995) Comparison of learning algorithms for handwritten digit recognition. Int Conf Artif Neural Netw 60:53–60

    Google Scholar 

  21. Lee SI, Lee H, Abbeel P, Ng AY (2006) Efficient \(\text{l}^{\tilde{~}}\) 1 regularized logistic regression. In: Proceedings of the National Conference on Artificial Intelligence, vol 21, p 401. AAAI Press, MIT Press, Menlo Park, Cambridge, London 1999

  22. Lewis DD (1998) Naive (Bayes) at forty: the independence assumption in information retrieval. In: Machine learning: ECML-98, pp 4–15. Springer

  23. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22

    Google Scholar 

  24. Liu C-L, Nakashima K, Sako H, Fujisawa H (2003) Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recognit 36(10):2271–2285

    Article  MATH  Google Scholar 

  25. Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431–441

    Article  MathSciNet  MATH  Google Scholar 

  26. Mohemmed A , Schliebs S, Matsuda S, Kasabov N (2012) Span: spike pattern association neuron for learning spatio-temporal spike patterns. Int J Neural Syst 22(04):1250012

    Article  Google Scholar 

  27. Orr MJL (1996) Introduction to radial basis neural networks. Center for Cognitive Science, Edinburgh University, Scotland. http://anc.ed.ac.uk/RBNN/

  28. Osuna E, Freund R, Girosi F (1997) Support vector machines: training and applications. Technical Report AIM-1602, MIT Artificial Intelligence Laboratory

  29. Paugam-Moisy H, Martinez R, Bengio S (2008) Delay learning and polychronization for reservoir computing. Neurocomputing 71(7):1143–1158

    Article  Google Scholar 

  30. Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Article  Google Scholar 

  31. Rekabdar B, Joorabian M, Shadgar B (2012) Artificial neural network ensemble approach for creating a negotiation model with ethical artificial agents. In: Artificial Intelligence and Soft Computing, pp 493–501. Springer

  32. Rekabdar B, Nicolescu M, Kelley R, Nicolescu M (2014) Unsupervised learning of spatio-temporal patterns using spike timing dependent plasticity. In: Goertzel B, Orseau L, Snaider J (eds) Artificial General Intelligence, vol 8598. Springer, pp 254–257

  33. Rekabdar B , Nicolescu M, Kelley R, Nicolescu M (2015) An unsupervised approach to learning and early detection of spatio-temporal patterns using spiking neural networks. J Intell Robot Syst 80:1–15

    Article  Google Scholar 

  34. Rekabdar B, Nicolescu M, Nicolescu M, Kelley R (2015) Scale and translation invariant learning of spatio-temporal patterns using longest common subsequences and spiking neural networks. In: International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1–7

  35. Rekabdar B, Nicolescu M, Nicolescu M, Kelley R (2015) A biologically inspired approach to learning spatio-temporal patterns. In: Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2015 Joint IEEE International Conference, 13–16 August 2015. IEEE, pp 291–297

  36. Rekabdar B, Nicolescu M, Nicolescu M, Saffar MT, Kelley R (2015) A scale and translation invariant approach for early classification of spatio-temporal patterns using spiking neural networks. Neural Process Lett 43(2):327–343

    Article  Google Scholar 

  37. Ruck DW, Rogers SK, Kabrisky M, Oxley ME, Suter BW (1990) The multilayer perceptron as an approximation to a bayes optimal discriminant function. IEEE Trans Neural Netw 1(4):296–298

    Article  Google Scholar 

  38. Simard PY, Steinkraus D, Platt JC (2003) Best practices for convolutional neural networks applied to visual document analysis. In: null, p 958. IEEE

  39. Song S, Miller KD, Abbott LF (2000) Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci 3(9):919–926

    Article  Google Scholar 

  40. Ude A (1993) Trajectory generation from noisy positions of object features for teaching robot paths. Robot Auton Syst 11(2):113–127

    Article  Google Scholar 

  41. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408

    MathSciNet  MATH  Google Scholar 

  42. Williams N, Zander S, Armitage G (2006) A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. ACM SIGCOMM Comput Commun Rev 36(5):5–16

    Article  Google Scholar 

  43. Yang J, Xu Y, Chen CS (1997) Human action learning via hidden Markov model. IEEE Trans Syst Man Cybern Part A Syst Hum 27(1):34–44

    Article  Google Scholar 

  44. Yao A, Gall J, Van Gool L (2010) A hough transform-based voting framework for action recognition. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2061–2068. IEEE

  45. Yu C-H, Ding W, Chen P, Morabito M (2014) Crime forecasting using spatio-temporal pattern with ensemble learning. In: Advances in Knowledge Discovery and Data Mining, pp 174–185. Springer

  46. Yu Q, Tang H, Tan KC, Li H (2013) Precise-spike-driven synaptic plasticity: learning hetero-association of spatio-temporal spike patterns. PLoS One 8(11):e78318

    Article  Google Scholar 

Download references

Acknowledgment

This work has been supported by ONR Award N000141210860.

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Authors and Affiliations

  1. Computer Science and Engineering Department, University of Nevada, Reno (UNR), Reno, NV, USA

    Banafsheh Rekabdar, Monica Nicolescu, Mircea Nicolescu & Sushil Louis

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  1. Banafsheh Rekabdar
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  2. Monica Nicolescu
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  3. Mircea Nicolescu
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  4. Sushil Louis
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Correspondence to Banafsheh Rekabdar.

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Rekabdar, B., Nicolescu, M., Nicolescu, M. et al. Using patterns of firing neurons in spiking neural networks for learning and early recognition of spatio-temporal patterns. Neural Comput & Applic 28, 881–897 (2017). https://doi.org/10.1007/s00521-016-2283-y

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  • DOI: https://doi.org/10.1007/s00521-016-2283-y

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