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)


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.


Mutual Information Poisson Process Spike Train Single Neuron Jump Process 
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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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