Nonlinear Dynamics

, Volume 81, Issue 4, pp 1951–1967 | Cite as

Artificial neural network-based modeling of brain response to flicker light

  • Razieh Falahian
  • Maryam Mehdizadeh Dastjerdi
  • Malihe Molaie
  • Sajad Jafari
  • Shahriar Gharibzadeh
Original Paper


Not only does the modeling of dynamical systems, for instance the biological systems, play an important role in the accurate perception and analysis of these systems, but it also makes the prediction and control of their behavior straightforward. The results of multiple researches in the field of the modeling of biological systems have indicated that the chaotic behavior is a prevalent feature of most complex interactive biological systems. Our results demonstrate that the artificial neural network provides us an effective means to model the underlying dynamics of these systems. In this paper, at first, we represent the results of the use of a multilayer feed-forward neural network to model some famous chaotic systems. The specified neural network is trained with the return maps extracted from the time series. We proceed with the paper by evaluating the accuracy and robustness of our model. The ability of the select neural network to model the dynamics of chosen chaotic systems is verified, even in the presence of noise. Afterwards, we model the brain response to the flicker light. It is known that the brain response to some stimuli such as the flicker light recorded as electroretinogram is an exemplar of chaotic behavior. The need remains, however, for realistic modeling of this behavior of the brain. In this paper, we represent the results of the modeling of this chaotic response by utilizing the proposed neural network. The capability of the neural network to model this specific brain response is confirmed.


Artificial neural network Bifurcation diagram Brain response Chaotic behavior Electroretinogram Modeling 



The authors would like to express their sincere gratitude to Professor Markus Meister in the department of molecular and cellular biology at Harvard University for providing the ERG data.


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Razieh Falahian
    • 1
  • Maryam Mehdizadeh Dastjerdi
    • 1
  • Malihe Molaie
    • 1
  • Sajad Jafari
    • 1
  • Shahriar Gharibzadeh
    • 1
  1. 1.Department of Biomedical EngineeringAmirkabir University of Technology (AUT)TehranIran

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