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Section DYNAMICS IN LIFE SCIENCES, NEUROSCIENCE APPLICATIONS WORKSHOP
References
1. Kauffman, Press Oxford University, 1993.
2. P. Eigen, M. Schuster, Springer-Verlag, 1979.
3. M. Eigen, J. McCaskill, and P. Schuster, J. Phys. Chem., 1988, 92(1), 6881-6891.
4. P. Boerlijst, M.C. Hogeweg, Phys. D Nonlinear Phenom., 1991, 48, 17-28.
5. Pan-Jun Kim and Hawoong Jeong, Phys. D, 2005, 203, 88-99.
6. J. Sardanyés and R. Solé, J. Theor. Biol., 2006, 243(4), 468-482.
7.M.B. Cronhjort, Phys. D, 1997, 101, 289-298.
Mechanical and Electrical Oscillations and Their Role in Sensory Hair Cells
A.B. Neiman1 * and R.M. Amro2
1 Department of Physics & Astronomy and Neuroscience Program, Ohio University, Athens, Ohio, USA;
2 Department of Physics & Astronomy, Ohio University, Athens, Ohio, USA. * Presenting e-mail: [email protected]
Background
Hair cells are mechanoreceptors which transduce mechanical vibrations to electrical signals in peripheral organs of senses of hearing and balance in vertebrates. Somatic cell motility and active motility of the hair bundle, mechanically sensitive structure on the hair cell apex, are two main mechanisms by which hair cells can amplify mechanical stimuli. In amphibians and some reptiles active processes in hair cells result in noisy mechanical oscillation of hair bundles, which may lead to frequency selective amplification. The same cells often demonstrate spontaneous electrical oscillation of their somatic potentials, a signature of yet another amplification mechanism. Functional role of voltage oscillation is not well understood.
Aims and Methods
We use computational modeling to address the following questions: (i) how the interaction of two distinct unequally noisy oscillators, mechanical and electrical, embedded in the hair cell, affect its spontaneous dynamics; (ii) how synchronous oscillatory activity helps to battle inevitable noise, and (iii) shapes sensitivity of amphibian hair cells to external mechanical signals. The model employs a Hodgkin-Huxley-type system for the basolateral electrical compartment [1] and a nonlinear hair bundle oscillator for the mechanical compartment [2], which are coupled bidirec-tionally. In the model, forward coupling is provided by the mechanoelectrical transduction current, flowing from the hair bundle to the cell soma. Backward coupling is due to reverse electromechanical transduction, whereby variations of the membrane potential affect adaptation processes in the hair bundle.
Conclusions
Despite noise, the stochastic hair bundle oscillations can be synchronized by external periodic force of few pN amplitude in a finite range of control parameters of the model. Furthermore, the hair bundle oscillations can be synchronized by oscillating receptor voltage [3]. Electrical and mechanical self-oscillations can result from bidirectional coupling [4], and their coherence of can be maximized by tuning the coupling strengths [4,5]. Consistent with previous experimental work [6], the model demonstrates that dynamical regimes of the hair bundle change in response to variations in the conductances of basolateral ion channels [4]. We show that sensitivity of the hair cell to weak mechanical stimuli can be maximized by varying coupling strengths, and that stochasticity of the hair bundle compartment is a limiting factor of the sensitivity [4].
Acknowledgements
This work was supported by the Condensed Matter and Surface Science Program, Neuroscience Program and the Quantitative Biology Institute at Ohio University. AN acknowledges support from the RSF (Russia) grant 14-4100044. The authors thank A. L. Shilinikov, B. Lindner, D. F. Russell, and M. H. Rowe for valuable discussions.
OM&P
OM&P
References
Section DYNAMICS IN LIFE SCIENCES, NEUROSCIENCE APPLICATIONS WORKSHOP
1. A.B. Neiman, K. Dierkes, B. Lindner, L. Han, and A.L. Shilnikov, The Journal of Mathematical Neuroscience, 2011, 1 (11), 1 - 24.
2. B. Nadrowski, P. Martin, and F. Jülicher, Proc. Natl. Acad. Sci. USA, 2004, 101, 12195 - 12200.
3. L. Han, and A.B. Neiman, Phys. Rev. E., 2010, 81(4), 041913.
4. R.M. Amro, and A.B. Neiman, Phys. Rev. E., 2014, 90(5), 052704.
5. R.M. Amro, B. Lindner, and A.B. Neiman, Phys. Rev. Lett., 2015, 115(3), 034101.
6. D. Ramunno-Johnson, C.E. Strimbu, A. Kao, L.F. Hemsing, D. Bozovic, Hearing Research, 2010, 268(1-2), 163 - 171.
Decision-Making Model and Experimental Study of the Influence of Stochastic Processes on Cognitive Brain Ability
Anastasiya E. Runnova*, Vadim V. Grubov, Alexey A. Koronovskii, Maria K. Kurovskaya, Alexander N. Pisarchik, Alexander E. Hramov
Yuri Gagarin State Technical University, Saratov, Russia. * Presenting e-mail: [email protected]
The perception of ambiguous images [1, 2] is just one, but a very exciting task among an enormous number of open problems which appeared during recent intensive brain studies. Visual perception was often studied through perceptual alternations while observing ambiguous images [3, 4], although perceptual alternations were also described for other modalities [4]. This phenomenon is also tightly connected with the problem of the categorical perception [17]. Though the underlying mechanism of image recognition is not yet well understood, the metastable visual perception is known to engage a distributed network of occipital, parietal and frontal cortical areas [5]. The generally accepted concept that throws light on this phenomenon involves noise inherent to neural brain cells activity, whose origin may be explained as the result of random neuron spikes [6]. Internal noise seems to play a crucial role in brain dynamics related to both the perception activity and other brain functions [4-6]. Different manifestations of stochastic processes in brain were extensively studied, including the perception of ambiguous images, in terms of simple stochastic processes like the Wiener process from the viewpoint of statistical properties [3-6]. At the moment, the important problem lies in developing ways to quantitatively measure noise characteristics that can open up plenty of new opportunities both in a study of the brain functionality and a diagnosis of its pathologies.
In the present work, we develop the quantitative theory and propose the experimental technique for measuring noise intensity related to the perception of ambiguous images. Both our theoretical findings and the proposed experimental approach are proved by psychological experiments.
The experimental studies were performed in accordance to the ethical standards. Forty healthy subjects from a group of unpaid volunteers, male and female, between the ages of 20 and 45 with the normal or corrected-to-normal visual acuity participated in the experiments. As an ambiguous image, we used the Necker cube illusion. The contrast of the three middle lines centered in the left middle corner, I£[0; 1], was considered as a control parameter. During the experiment Necker cube images with different wireframe contrasts, i.e. with the different values of the control parameter I (Fig. 1), were repeatedly showed to a person in a random sequence, with each cube being placed in the middle of a computer screen as black lines on a white background. All participants were well aware about two possible orientations of the Necker cube, and both were really seen by all of them. All participants were instructed to press either the left or the right key on the control panel according to their first visual impression (left-oriented cube (Fig. 1(a)) or right-oriented cube (Fig. 1(e), respectively). Both the image presentation and the recording of personal responses were accomplished with the help of the equipment being a part of Electroencephalograph-recorder Encephalan-EEGR-19/26 (Medicom MTD).
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1=0 1=0.29 /=0.5 /=0.78 /= 1 ->-
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Fig.1. Examples of distinct Necker cube images with different wireframe contrasts characterized by control parameter I
