Skip to main content

A Single Neuron Model for Pattern Classification

  • Conference paper
Neural Information Processing (ICONIP 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7666))

Included in the following conference series:

  • 4320 Accesses

Abstract

A biologically realistic non linear integrate and fire model is proposed in this paper. Its complete solution is derived and used for the construction of aggregation function in Multi layer perceptron model for classification of UCI Machine learning datasets. It is found that a single neuron in the conventional neural network is sufficient for the classification datasets. It has been observed that the proposed neuron model is far superior in terms of classification accuracy when compared with single integrate and fire neuron model. It is observed that biological phenomenon makes artificial neural network efficient for the classification.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Abbott, L.F., van Vreeswijk, C.: Asynchronous states in a network of pulse-coupled oscillators. Phys. Rev. E 48, 1483–1490 (1993)

    Article  Google Scholar 

  2. Abbott, L.F.: Lapique’s introduction of the integrate-and-fire model neuron. Brain Research Bulletin 50(5/6), 303–304 (1907)

    Google Scholar 

  3. Brette, R., Gerstner, W.: Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J. Neurophysiol. 94, 3637–3642 (2005)

    Article  Google Scholar 

  4. FitzHugh, R.: Impulses and physiological states in theoretical models of nerve membrane. Biophysical J. 1, 445–466 (1961)

    Article  Google Scholar 

  5. Hindmarsh, J.L., Rose, R.M.: A model of neuronal bursting using three coupled first order differential equations. Proc. R. Soc. Lond. B. 221, 87–102 (1984)

    Article  Google Scholar 

  6. Hodgkin, A., Huxley, A.: A quantitative description of membrane current and its appli-cation to conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952)

    Google Scholar 

  7. Ermentrout, G.B., Kopell, N.: Parabolic bursting in an excitable system coupled with a slow oscillation. SIAM J. Appl. Math. 46, 233–253 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  8. Maass, W.: Computation with spiking neurons. In: The Handbook of Brain Theory and Neural Networks, 2nd edn., pp. 1080–1083. MIT Press, Cambridge (2003)

    Google Scholar 

  9. Morris, C., Lecar, H.: Voltage Oscillations in the barnacle giant muscle fiber. Biophys J. 35, 193–213 (1981)

    Article  Google Scholar 

  10. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)

    Article  Google Scholar 

  11. Stein, R.B.: A theoretical analysis of neuronal variability. Biophys. J. 5, 173–194 (1965)

    Article  Google Scholar 

  12. Yadav, A., Mishra, D., Ray, S., Yadav, R.N., Kalra, P.K.: Learning with Single Integrate-and-Fire Neuron. In: Proceedings of IEEE International Joint Conference on Neural Networks, vol. 4, pp. 2156–2161 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chandra, B., Naresh Babu, K.V. (2012). A Single Neuron Model for Pattern Classification. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_75

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34478-7_75

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34477-0

  • Online ISBN: 978-3-642-34478-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics