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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Saarinen, A., Linne, M.-L., Yli-Harja, O.: Modeling single neuron behavior using stochastic differential equations. Journal of Neurocomputing 69(10-12) (June 2006)
Dayan, P., Abbott, L.F.: Theoretical Neuroscience “Computational and Mathematical Modeling of neural system”. MIT Press (2001)
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)
Gerstner, W., Kistler, W.M.: Spiking neuron models. Cambridge Univ. Press, Cambridge (2002)
Deco, G., Schürmann, B.: Information Transmission and Temporal Code in Central Spiking Neurons. Physical Review Letters 79(23) (December 8, 1997)
Deco, G., Schürmann, B.: Information Dynamics: Foundation and Applications. Springer (2001)
Kistler, W., Gerstner, W., et al.: Reduction of Hodking_Huxley equations to a single variable threshold model. Neural Computation 9, 1015–1045 (1997)
Koch, C.: Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, New York (1998)
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)
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)
Softky, W., Koch, C.: J. Neuroscience 13, 334 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer India
About this paper
Cite this paper
Kumar, S. (2013). Simulating Spiking Neuron for Information Theoretic Analysis in Stochastic Neuronal System. In: Kumar M., A., R., S., Kumar, T. (eds) Proceedings of International Conference on Advances in Computing. Advances in Intelligent Systems and Computing, vol 174. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0740-5_23
Download citation
DOI: https://doi.org/10.1007/978-81-322-0740-5_23
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0739-9
Online ISBN: 978-81-322-0740-5
eBook Packages: EngineeringEngineering (R0)