Skip to main content
Log in

An Unsupervised Approach to Learning and Early Detection of Spatio-Temporal Patterns Using Spiking Neural Networks

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

This paper addresses the problem of learning and recognizing spatio-temporal patterns, which are typically encountered when representing gestures or other human actions. Existing approaches to learning such patterns are typically supervised, rely on extensive amounts of training data and require the observation of the entire pattern for recognition. We propose an approach that brings the following main contributions: i) it learns the patterns in an unsupervised manner, ii) it uses a very small number of training samples, and iii) it enables early classification of the pattern from observing only a small fraction of the pattern. The proposed method relies on spiking networks with axonal conductance delays, which learn encoding of individual patterns as sets of polychronous neural groups. Classification is performed using a similarity metric between sets, based on a modified version of the Jaccard index. The approach is evaluated on a data set of hand-drawn digits that encode the temporal information on how the digit has been drawn. In addition, the method is compared with three other standard pattern classification methods: support vector machines, logistic regression with regularization and ensemble neural networks, all trained with the same data set. The results show that the proposed approach can successfully learn these patterns from a significantly small number of training samples, can identify patterns before their completion, and it performs better than or comparable with the three other supervised methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

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

    Article  Google Scholar 

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

    Google Scholar 

  3. Charniak, E., Goldman, R.P.: A bayesian model of plan recognition. Artif. Intell. 64(1), 53–79 (1993)

    Article  Google Scholar 

  4. Demiris, Y.: Prediction of intent in robotics and multi-agent systems. Cogn. Process. 8(3), 151–158 (2007)

    Article  Google Scholar 

  5. Izhikevich, E.M.: Polychronization: computation with spikes. Neural Comput. 18(2), 245–282 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  6. Izhikevich, E.M. et al.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14(6), 1569–1572 (2003)

    Article  Google Scholar 

  7. Izhikevich, E.M., Gally, J.A., Edelman, G.M.: Spike-timing dynamics of neuronal groups, vol. 14, pp 933–944 (2004)

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

    Article  Google Scholar 

  9. Kandel, E.R., Schwartz, J.H., Jessell, T.M., et al.: Principles of neural science, vol. 4. McGraw-Hill, New York (2000)

    Google Scholar 

  10. Karimpouli, S., Fathianpour, N., Roohi, J.: A new approach to improve neural networks’ algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (scmnn). J. Pet. Sci. Eng. 73(3), 227–232 (2010)

    Article  Google Scholar 

  11. Kelley, R., King, C., Tavakkoli, A., Nicolescu, M., Nicolescu, M., Bebis, G.: An architecture for understanding intent using a novel hidden markov formulation. Int. J. Humanoid Robot. 5(02), 203–224 (2008)

    Article  Google Scholar 

  12. Lee, S.-I., Lee, H., Abbeel, P., Ng, A.Y.: Efficient l˜ 1 regularized logistic regression. In: Proceedings of the National Conference on Artificial Intelligence, Vol. 21, p 401. AAAI Press, MIT Press, Menlo Park, Cambridge (1999,2006)

  13. Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT press (1999)

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

    Article  MATH  MathSciNet  Google Scholar 

  15. Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT Press (2012)

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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

  20. Rekabdar, B., Shadgar, B., Osareh, A.: Learning teamwork behaviors approach: learning by observation meets case-based planning. In: Artificial Intelligence: Methodology, Systems, and Applications, pp 195–201. Springer (2012)

  21. Ross, S.M.: Introduction to probability and statistics for engineers and scientists. Academic Press (2009)

  22. Schaal, S.: Is imitation learning the route to humanoid robots?. Trends Cogn. Sci. 3(6), 233–242 (1999)

    Article  Google Scholar 

  23. Szatmáry, B., Izhikevich, E.M.: Spike-timing theory of working memory. PLoS Comput. Biol. 6(8), e1000879 (2010)

    Article  Google Scholar 

  24. Tao, X., Michel, H.E.: Data clustering via spiking neural networks through spike timing-dependent plasticity. In: IC-AI, pp. 168–173 (2004)

  25. Thrun, S., Burgard, W., Fox, D.: Probabilistic robotics (intelligent robotics and autonomous agents series). Intelligent Robotics and Autonomous Agents, The MIT Press (August 2005) (2005)

  26. Woodward, A.L., Sommerville, J.A., Guajardo, J.J.: How infants make sense of intentional action. In: Intentions and Intentionality: Foundations of Social Cognition, pp. 149–169 (2001)

  27. Rekabdar, B., Nicolescu, M., Kelley, R., Nicolescu, M.: Unsupervised learning of spatio-temporal patterns using spike timing dependent plasticity. In: Artificial General Intelligence, pp. 254–257. Springer (2014)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Banafsheh Rekabdar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rekabdar, B., Nicolescu, M., Kelley, R. et al. An Unsupervised Approach to Learning and Early Detection of Spatio-Temporal Patterns Using Spiking Neural Networks. J Intell Robot Syst 80 (Suppl 1), 83–97 (2015). https://doi.org/10.1007/s10846-015-0179-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-015-0179-1

Keywords

Navigation