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A Hopfield neural network with multi-compartmental activation

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Abstract

The Hopfield network is a form of recurrent artificial neural network. To satisfy demands of artificial neural networks and brain activity, the networks are needed to be modified in different ways. Accordingly, it is the first time that, in our paper, a Hopfield neural network with piecewise constant argument of generalized type and constant delay is considered. To insert both types of the arguments, a multi-compartmental activation function is utilized. For the analysis of the problem, we have applied the results for newly developed differential equations with piecewise constant argument of generalized type beside methods for differential equations and functional differential equations. In the paper, we obtained sufficient conditions for the existence of an equilibrium as well as its global exponential stability. The main instruments of investigation are Lyapunov functionals and linear matrix inequality method. Two examples with simulations are given to illustrate our solutions as well as global exponential stability.

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Acknowledgments

The authors wish to express their sincere gratitude to the referees for the helpful criticism and valuable suggestions, which helped to improve the paper significantly.

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Authors and Affiliations

  1. Department of Mathematics, Middle East Technical University, 06800, Ankara, Turkey

    Marat U. Akhmet & Meltem Karacaören

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  1. Marat U. Akhmet
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  2. Meltem Karacaören
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Correspondence to Marat U. Akhmet.

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Akhmet, M.U., Karacaören, M. A Hopfield neural network with multi-compartmental activation. Neural Comput & Applic 29, 815–822 (2018). https://doi.org/10.1007/s00521-016-2597-9

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  • DOI: https://doi.org/10.1007/s00521-016-2597-9

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