Skip to main content
SpringerLink
Log in

Prospects for the Development of Neuromorphic Systems

  • Conference paper
  • First Online:
Advances in Neural Computation, Machine Learning, and Cognitive Research (NEUROINFORMATICS 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 736))

Included in the following conference series:

Abstract

The article is devoted to the analysis of neural networks from the positions of the neuromorphic approach. The analysis allows to conclude that modern artificial neural networks can effectively solve particular problems, for which it is permissible to fix the topology of the network or its small changes. In the nervous system, as a prototype, the functional element - the neuron - is a fundamentally complex object, which allows implementing a change in topology through the structural adaptation of the dendritic tree of a single neuron. Promising direction of development of neuromorphic systems based on deep spike neural networks in which structural adaptation can be realized is determined.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran J.: cuDNN: Efficient Primitives for Deep Learning arXiv:1410.0759v3 [cs.NE], 18 December 2014

  2. Pfeil, T., Grübl, A., Jeltsch, S., Müller, E., Müller, P., Petrovici, M.A., Schmuker, M., Brüderle, D., Schemmel, J., Meier, K.: Six networks on a universal neuromorphic computing substrate. Front. Neurosci. 7(11) (2013). doi:10.3389/fnins.2013.00011

  3. Schemmel, J., Bruderle, D., Grubl, A., Hock, M., Meier, K., Millner, S.: A wafer-scale neuromorphic hardware system for large-scale neural modeling. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1947–1950. IEEE (2010)

    Google Scholar 

  4. Furber, S.B., Lester, D.R., Plana, L., Garside, J.D., Painkras, E., Temple, S., Brown, A.D., et al.: Overview of the spinnaker system architecture. IEEE Trans. Comput. 62(12), 2454–2467 (2013)

    Article  MathSciNet  Google Scholar 

  5. Schuman, C.D., Birdwell, J.D.: Variable structure dynamic artificial neural networks. Biol. Inspired Cognit. Archit. 6, 126–130 (2013)

    Article  Google Scholar 

  6. Schuman, C.D., Disney, A., Reynolds, J.: Dynamic adaptive neural network arrays: a neuromorphic architecture. In: Workshop on Machine Learning in HPC Environments, Supercomputing (2015)

    Google Scholar 

  7. Benjamin, B.V., Gao, P., McQuinn, E., Choudhary, S., Chandrasekaran, A.R., Bussat, J.-M., Alvarez-Icaza, R., Arthur, J.V., Merolla, P., Boahen, K., et al.: Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE. 102(5), 699–716 (2014)

    Article  Google Scholar 

  8. Merolla, P.A., Arthur, J.V., Alvarez-Icaza, R., Cassidy, A.S., Sawada, J., Akopyan, F., Jackson, B.L., Imam, N., Guo, C., Nakamura, Y., et al.: A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345(6197), 668–673 (2014)

    Article  Google Scholar 

  9. Van Veen, F.: The Neural Network Zoo (2016). http://www.asimovinstitute.org/neural-network-zoo/. Accessed 16 Apr 2017

  10. Kingma, D.P., Welling, M.: Auto-encoding Variational Bayes. arXiv preprint arXiv:1312.6114 (2013)

  11. Hayes, B.: First links in the Markov chain. Am. Sci. 101(2), 252 (2013)

    Google Scholar 

  12. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. 79(8), 2554–2558 (1982)

    Article  MathSciNet  Google Scholar 

  13. Hinton, Geoffrey E., Sejnowski, Terrence J.: Learning and releaming in Boltzmann machines. Parallel Distrib. Process. 1, 282–317 (1986)

    Google Scholar 

  14. LeCun, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE. 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  15. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Published as a Conference Paper at ICLR 2015

    Google Scholar 

  16. Szegedy, C., et al.: Rethinking the Inception Architecture for Computer Vision. arXiv:1512.00567v3 [cs.CV], 11 December 2015

  17. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  18. He, K., et al.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)

  19. Moniz, J., Pal, C.: Convolutional Residual Memory Networks. arXiv:1606.05262 [cs.CV], 14 July 2016

  20. Targ, S., Almeida, D., Lyman, K.: Generalizing Residual Architectures. arXiv:1603.08029v1 [cs.LG], 25 March 2016

  21. Kohonen, Teuvo: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43(1), 59–69 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  22. Deng, D., Kasabov, N.: ESOM: an algorithm to evolve self-organizing maps from on-line data streams. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2000), Como, Italy, 24–27 July 2000, vol. vi, pp. 3–8. IEEE computer society (2000)

    Google Scholar 

  23. Growing Hierarchical Self-Organizing Map (GHSOM), Dittenbach, M., Merkl, D., Rauber, A.: The growing hierarchical self-organizing map. In: Proceedings of the International Joint Conference On Neural Networks (IJCNN 2000), vol. VI, pp. 15–19

    Google Scholar 

  24. Self-Organizing Surfaces (SOS), Zell, A., Bayer, H., Bauknecht, H.: Similarity analysis of molecules with self-organizing surfaces—an extension of the self-organizing map. In: Proceedings of International Conference on Neural Networks, ICNN 1994, Piscataway, pp. 719–724 (1994)

    Google Scholar 

  25. Furao, S., Hasegawa, O.: An incremental network for on-line unsupervised classification and topology learning. Neural Netw. 19(1), 90–106 (2006)

    Article  MATH  Google Scholar 

  26. Martinetz, T., Schulten, K.: A “neural gas” Network Learns Topologies. Artificial Neural Networks, pp. 397–402. Elsevier, Amsterdam (1991)

    Google Scholar 

  27. Fritzke, B.: A growing neural gas network learns topologies. Adv. Neural. Inf. Process. Syst. 7, 625–632 (1995)

    Google Scholar 

  28. Prudent, Y., Ennaji, A.: An incremental growing neural gas learns topologies. In: Neural Networks, IJCNN 2005 (2005)

    Google Scholar 

  29. Fritzke, B.: Growing cell structures- a self-organizing network for unsupervised and supervised learning. Neural Netw. 7(9), 1441–1460 (1994)

    Article  Google Scholar 

  30. Hodge, V., Austin, J.: Hierarchical growing cell structures: TreeGCS. In: IEEE TKDE Special Issue on Connectionist Models for Learning in Structured Domains

    Google Scholar 

  31. Vlassis, N., Dimopoulos, A., Papakonstantinou, G.: The probabilistic growing cell structures algorithm. Lecture Notes in Computer Science, vol. 1327, p. 649 (1997)

    Google Scholar 

  32. Hunsberger, E., Eliasmith, C.: Spiking deep networks with LIF neurons. arXiv:1510.08829v1 [cs.LG], 29 October 2015

  33. Gavrilov, A., Panchenko, K.: Methods of learning for spiking neural networks. A survey. In: 13th International Scientific-Technical Conference APEIE–39281, At Novosibirsk, vol. 1, part 2, pp. 60–65 (2016)

    Google Scholar 

  34. Cao, Y., Chen, Y., Khosla, D.: Spiking deep convolutional neural networks for energy-efficient object recognition. Int. J. Comput. Vis. 113(1), 54–66 (2015)

    Article  MathSciNet  Google Scholar 

  35. Hodgkin, A.L., Huxley, A.F.: A quantative description of membrane current and its application conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952)

    Article  Google Scholar 

  36. Izhikevich E.M.: Simple model of spiking neurons. In: IEEE Transactions on Neural Networks. A Publication of the IEEE Neural Networks Council, vol. 14 (2003)

    Google Scholar 

  37. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)

    Article  MathSciNet  MATH  Google Scholar 

  38. Nair, V., Hinton, G.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, 21–24 June 2010

    Google Scholar 

  39. Burkitt, A.N.: A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Biol. Cybern. 95(1), 1–19 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  40. Bakhshiev, A.V., Gundelakh, F.V.: Application the spiking neuron model with structural adaptation to describe neuromorphic systems. Procedia Comput. Sci. 103, 190–197 (2017)

    Article  Google Scholar 

  41. Nicholls, J.G., Martin, A.R., Fuchs, P.A., Brown, D.A., Diamond, M.E., Weisblat, D.A.: From Neuron to Brain. Sinauer Associates Incorporated, Sunderland (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Russian State Scientific Center for Robotic and Technical Cybernetics, Saint-Petersburg, 195257, Russian Federation

    Aleksandr Bakhshiev

  2. Peter the Great Saint-Petersburg Polytechnic University, Saint-Petersburg, 195251, Russian Federation

    Lev Stankevich

Authors
  1. Aleksandr Bakhshiev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lev Stankevich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aleksandr Bakhshiev .

Editor information

Editors and Affiliations

  1. Scientific Research Institute for System Analysis, Russian Academy of Sciences, Moscow, Russia

    Boris Kryzhanovsky

  2. Scientific Research Institute for System Analysis, Russian Academy of Sciences, Moscow, Russia

    Witali Dunin-Barkowski

  3. Scientific Research Institute for System Analysis, Russian Academy of Sciences, Moscow, Russia

    Vladimir Redko

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Bakhshiev, A., Stankevich, L. (2018). Prospects for the Development of Neuromorphic Systems. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research. NEUROINFORMATICS 2017. Studies in Computational Intelligence, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-66604-4_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-66604-4_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66603-7

  • Online ISBN: 978-3-319-66604-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation