Is Attention a “Period Window” in the Chaotic Brain?

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Back to table of contents Previous article Next article LettersFull AccessIs Attention a “Period Window” in the Chaotic Brain?Sajad Jafari, Ph.D. candidate, Zohreh Ansari, Ph.D. student, Seyyed Mohammad Reza Hashemi Golpayegani, Ph.D., and Shahriar Gharibzadeh, M.D., Ph.D.Sajad JafariSearch for more papers by this author, Ph.D. candidate, Zohreh AnsariSearch for more papers by this author, Ph.D. student, Seyyed Mohammad Reza Hashemi GolpayeganiSearch for more papers by this author, Ph.D., and Shahriar GharibzadehSearch for more papers by this author, M.D., Ph.D.Published Online:1 Jan 2013https://doi.org/10.1176/appi.neuropsych.11120366AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To the Editor: Artificial neural networks (ANNs), introduced in the fields of mathematics and engineering, are inspired from biological neural networks. They have abilities like those of the neural network system of the human. Among these abilities, the most important one is ability in learning and generalization. Therefore, the ANNs have become implemented in a variety of fields, such as pattern recognition, classification, and control.1Some researchers have shown that brain signals (EEGs) have deterministic, chaotic properties.2 In order to make ANNs more similar to the real nervous system (and thus taking more advantages of its abilities), chaotic neural networks were introduced.3 It was observed in more recent research that, during attention, EEG signals are nearly periodic and more ordered, as compared with the resting state, in which brain behavior is more chaotic.4 However, there is no model that includes this phenomenon.On the other hand, almost all of the chaotic systems have some parameters in which specific changes in their values may affect the behavior of the system, not only quantitatively, but also qualitatively. In other words, changing the values of these parameters may cause the system to bifurcate. Some chaotic systems have an interesting characteristic: the system suddenly shows periodic behavior when it is in chaotic mode. These periodic behaviors belong to small regions called “period windows” that are embedded in the chaotic region of the bifurcation diagram of the system.5We propose that the periodic behavior during attention could be period windows embedded in the chaotic brain. Hence, designing chaotic ANNs and creating periodic windows in them may help neuroscientists in studying attention, and, specifically, the learning that occurs during attention. Such models will probably be applicable in diagnosing attention disorders. Surely, experimental researches are needed to validate such ANNs.Biomedical Engineering FacultyAmirkabir University of TechnologyReferences1 Anderson JA: Introduction to Neural Networks. Cambridge, MA, MIT Press, 1995Google Scholar2 Freeman WJ: Strange attractors that govern mammalian brain dynamics shown by trajectories of electroencephalographic (EEG) potential. IEEE Trans Circ Syst 1998; 35:781–783Crossref, Google Scholar3 Wang L: Interactions between neural networks: a mechanism for tuning chaos and oscillations. Cogn Neurodyn 2007; 1:185–188Crossref, Medline, Google Scholar4 Freeman WJ: The Physiology of Perception. Sci Am 1991; 264:78–85Crossref, Medline, Google Scholar5 Hilborn RC: Chaos and Nonlinear Dynamics: An Introduction for Scientists and Engineers, 2nd Edition. London, UK, Oxford University Press, 2001Google Scholar FiguresReferencesCited byDetailsCited byThe Effect of 40 Hz Binaural Beats on Working MemoryIEEE Access, Vol. 10Higuchi fractal dimension: An efficient approach to detection of brain entrainment to theta binaural beatsBiomedical Signal Processing and Control, Vol. 68Effects of Low and High Neuron Activation Gradients on the Dynamics of a Simple 3D Hopfield Neural Network21 September 2020 | International Journal of Bifurcation and Chaos, Vol. 30, No. 11Chaotic flows with special equilibria26 March 2020 | The European Physical Journal Special Topics, Vol. 229, No. 6-7Detecting bifurcation points in a memristive neuron model14 October 2019 | The European Physical Journal Special Topics, Vol. 228, No. 10Nonlinear Dynamics, Vol. 92, No. 4Nonlinear Dynamics, Vol. 88, No. 2Communications in Nonlinear Science and Numerical Simulation, Vol. 44Toward a complex system understanding of bipolar disorder: A chaotic model of abnormal circadian activity rhythms in euthymic bipolar disorder11 July 2016 | Australian & New Zealand Journal of Psychiatry, Vol. 50, No. 8Nonlinear Dynamics, Vol. 83, No. 1-2Chronobiology International, Vol. 33, No. 4Communications in Nonlinear Science and Numerical Simulation, Vol. 29, No. 1-3 Volume 25Issue 1 Winter 2013Pages E05-E05 Metrics PDF download History Published online 1 January 2013 Published in print 1 January 2013