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Simulating Spiking Neuron for Information Theoretic Analysis in Stochastic Neuronal System

  • Sanjeev Kumar
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 174)

Abstract

A neural model is used to analyze decoding of information from response and reproducing the response from a given stimuli. Extended leaky integrate and fire (LIF) model of neuron proposed by Deco and Scurmann is analyzed to study the effects of diffusion and jump process. Relationship in generated spikes and spike firing rate required to encode stimulus is validated. We have taken input stimuli spike train to be generated by Poisson process and studied the entropy of Poisson process during a small time window. We examined the information theoretic framework to simulate the coding strategy of single neuron for separating two different input spikes trains with use of information theory. Simulations have done to detect the number of output spikes required to differentiates between input signals without decoding the neural code.

Keywords

Mutual Information Poisson Process Spike Train Single Neuron Jump Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Saarinen, A., Linne, M.-L., Yli-Harja, O.: Modeling single neuron behavior using stochastic differential equations. Journal of Neurocomputing 69(10-12) (June 2006)Google Scholar
  2. 2.
    Dayan, P., Abbott, L.F.: Theoretical Neuroscience “Computational and Mathematical Modeling of neural system”. MIT Press (2001)Google Scholar
  3. 3.
    Gabbiani, C., Koch, C.: Principles of Spike Train Analysis in Methods in Neural Modeling: From Ions to Network. In: Koch, C., Segev, I. (eds.). MIT Press (1998); Burkitt, A.: A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Biological Cybernetics 95(1), 1–19 (2006)Google Scholar
  4. 4.
    Gerstner, W., Kistler, W.M.: Spiking neuron models. Cambridge Univ. Press, Cambridge (2002)MATHCrossRefGoogle Scholar
  5. 5.
    Deco, G., Schürmann, B.: Information Transmission and Temporal Code in Central Spiking Neurons. Physical Review Letters 79(23) (December 8, 1997)Google Scholar
  6. 6.
    Deco, G., Schürmann, B.: Information Dynamics: Foundation and Applications. Springer (2001)Google Scholar
  7. 7.
    Kistler, W., Gerstner, W., et al.: Reduction of Hodking_Huxley equations to a single variable threshold model. Neural Computation 9, 1015–1045 (1997)CrossRefGoogle Scholar
  8. 8.
    Koch, C.: Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, New York (1998)Google Scholar
  9. 9.
    Di Maio, V., Lansky, P., Rodriguez, R.: Different Types of Noise in Leaky Integrate and fire model of Neuronal Dynamics with Discrete Periodical Input. Gen. Physiol. Biophys. 23, 21–38 (2004)Google Scholar
  10. 10.
    Strong, S.P., Koberle, R., de Ruyter van Steveninck, R.R., Bialek, W.: Entropy and Information in Neural Spike Trains. Physical Review Letters 80(1) (January 1998)Google Scholar
  11. 11.
    Softky, W., Koch, C.: J. Neuroscience 13, 334 (1993)Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • Sanjeev Kumar
    • 1
    • 2
  1. 1.Jawaharlal Nehru UniversityNew DelhiIndia
  2. 2.Krishna Institute of Engineering and TechnologyGhaziabadIndia

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