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Analysis of pulse period for passive neuron in pulse coupled neural network

Author

Listed:
  • Nie, Rencan
  • Cao, Jinde
  • Zhou, Dongming
  • Qian, Wenhua

Abstract

This paper investigates the passive pulse period for the passive neuron in discrete PCNN. We first define a dynamic comparative ratio instead of the logical comparison to describe the linear difference between neural inner state and dynamic threshold. Then a nearly accurate formula about the passive pulse period is given by using the max lower limit of dynamic comparative ratios, and the rationality of which is proved based on the error analysis between estimated and real passive pulse periods. Moreover, we deduce a stable pulse period from estimated pulse period such that the neuron could nonperiodically and periodically pulse in two different time phases, successively. Further, the initial phase, from which the passive neuron can start to pulse periodically, is estimated. Some examples are performed, and the results reach the consensus with theoretical analyses.

Suggested Citation

  • Nie, Rencan & Cao, Jinde & Zhou, Dongming & Qian, Wenhua, 2019. "Analysis of pulse period for passive neuron in pulse coupled neural network," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 277-289.
    • Handle: RePEc:eee:matcom:v:155:y:2019:i:c:p:277-289
    DOI: 10.1016/j.matcom.2018.05.009
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    References listed on IDEAS

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    1. Liu, Linna & Zhu, Quanxin, 2015. "Almost sure exponential stability of numerical solutions to stochastic delay Hopfield neural networks," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 698-712.
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    Cited by:

    1. Sun, Li & Zhu, Haitao & Ding, Yanhui, 2020. "Impulsive control for persistence and periodicity of logistic systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 294-305.

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