- Open Access
Analytical Insights on Theta-Gamma Coupled Neural Oscillators
© L. Fontolan et al.; licensee Springer 2013
- Received: 11 March 2013
- Accepted: 13 June 2013
- Published: 14 August 2013
In this paper, we study the dynamics of a quadratic integrate-and-fire neuron, spiking in the gamma (30–100 Hz) range, coupled to a delta/theta frequency (1–8 Hz) neural oscillator. Using analytical and semianalytical methods, we were able to derive characteristic spiking times for the system in two distinct regimes (depending on parameter values): one regime where the gamma neuron is intrinsically oscillating in the absence of theta input, and a second one in which gamma spiking is directly gated by theta input, i.e., windows of gamma activity alternate with silence periods depending on the underlying theta phase. In the former case, we transform the equations such that the system becomes analogous to the Mathieu differential equation. By solving this equation, we can compute numerically the time to the first gamma spike, and then use singular perturbation theory to find successive spike times. On the other hand, in the excitable condition, we make direct use of singular perturbation theory to obtain an approximation of the time to first gamma spike, and then extend the result to calculate ensuing gamma spikes in a recursive fashion. We thereby give explicit formulas for the onset and offset of gamma spike burst during a theta cycle, and provide an estimation of the total number of spikes per theta cycle both for excitable and oscillator regimes.
- Dynamical systems
- Geometric singular perturbation theory
- Blow-up method
- Spike times
- Theta-gamma rhythms
- Type I neuron
- SNIC bifurcation
Oscillations of neural activity are ubiquitous in the brain in many frequency bands , and it has been often argued that they play a functional role in cortical processing [2–4]. Physiological experiments and computational models have shown that ongoing brain oscillations are involved in sensory-motor functions , synaptic plasticity , memory formation and maintenance , among many other cognitive tasks. Indeed, it has been reported  that intrinsic brain rhythms can bias input selection, temporally link neurons into assemblies, and facilitate mechanisms that cooperatively support temporal representation and long-term consolidation of information. Notably gamma oscillations (>30 Hz) are prominent in neocortex during attention , sensory processing [9, 10], or motor control tasks , together with slower rhythms in the theta (3–8 Hz) or delta (1–3 Hz) range that have also been linked to various aspects of cognitive processes like working memory or the transmission of sensory and motor signals.
Many recent contributions point to nontrivial interactions among different frequency bands [12–14], such as phase-amplitude [15, 16] or phase-phase coupling [17, 18] that can facilitate the simultaneous integration of multiple layers of information . The hippocampus is a privileged site for observing such interactions [11, 20], since theta and gamma waves are particularly strong and reliable in that region . Another particular case is represented by perception of speech signal performed by auditory cortex. In fact, to capture the many different relevant features of speech (i.e., syllables, vowels, consonants, etc.), the brain must be able to parse the speech signal over these many time-scales at the same time. A number of recent works introduced the hypothesis that a network of nested theta (3–8 Hz) and gamma (30–100 Hz) rhythms could accomplish this task [22–24], given their matching in frequency with syllabic and phonemic time-scale, respectively. Since there is no external onset signaling the presence of an incoming syllabic content, the phase of the gamma rhythm needs to be reset by some intrinsic mechanism, e.g., by theta input . It becomes therefore important to know the time to first spike, which would be a measure of the speed of gamma phase resetting, as well as the time to last spike and the spiking frequency during excitable period.
There is a large literature on mathematical analysis of single frequency oscillators in networks of cortical circuits [25–31], and much work has been done in computational modeling of neural oscillations [2, 32, 33]. There is also a significant number of mathematical studies on cross-frequency interactions, however, most of that analysis is limited to the cases of weak coupling [34–37]. Strong coupling case has been analyzed either with pulsatile coupling [25, 38, 39] or with semianalytical and computational techniques [40–42]. Importantly, the question of how strong continuous coupling between slow and fast oscillations influences frequency and time of fast spikes has not been treated analytically, at least to the best of our knowledge. Yet experimental data suggest that phase-amplitude coupling in the brain is continuous (i.e., low-frequency phase is conveyed through local field potential, a continuous signal) and strong [15, 39, 41], so this will be the regime we aim to study in the present work.
In this article, we provide analytical insights on the precise spiking times of a simplified Pyramidal Interneuron Network Gamma (PING)  during theta modulation. Two separate cases are studied: In the first setting, which we will refer to as oscillatory regime, the gamma network behaves as an intrinsic oscillator whose spike frequency is modulated by the theta phase; in the second, named excitable regime, gamma spikes are only evoked when input coming from the theta oscillator is strong enough. In the latter case, the system is in an “excitable” regime, where theta pushes gamma back and forth across a Saddle-Node on Invariant Circle (SNIC) bifurcation. The analysis can be generalized beyond theta-gamma nested oscillations; indeed it describes any coupling between low and a high frequency rhythms , provided that the latter is produced through feedback inhibition to the excitatory cell. To compute the time to the first gamma spike, we used different approaches for the two regimes: In the oscillatory case, we reduce the system in order to describe its dynamics with the Mathieu equation , and in the excitable case we apply an extension of geometric singular perturbation theory [45–47]. We then use a combination of the two to get successive spike times and an estimation of the total number of spikes per theta cycle.
In Sect. 1, we introduce the system to be studied.
In Sect. 2, we consider the system in the oscillatory regime and compute time to first gamma spike using Mathieu functions. We found that spike time is mainly determined by the magnitude of theta-gamma coupling (λ) and of theta frequency.
In Sect. 3, we turn our attention to the excitable regime where theta phase determines the magnitude of input, thereby causing the gamma circuit to spike.
Finally, we show that our approach gives results in agreement with direct numerical simulations of the system of interest.
In our analysis, we use tools from geometric singular perturbation theory. This approach normally fails in proximity of nonhyperbolic points, as it would be the case for the system considered in the present paper, but the blow-up method extension provided in  allows us to compute approximations of the passage time to the first spike in the excitable case, and it is used both in the oscillator and excitable cases to estimate the duration of inhibition and the passage time of subsequent spikes. The latter estimates are based on the idea that inhibition puts the system in a state of quasi equilibrium; consequently, they work well if inhibition is strong and excitation not too high.
where is the instantaneous phase of the slow rhythm variable (delta/theta frequency band, i.e., 1–8 Hz), which provides the sinusoidal modulatory input to the EG cell; is the variable representing the activation of the inhibitory synapse; represents constant driving input to excitatory gamma neuron; λ is the strength of theta-gamma coupling; is the inhibitory synaptic strength; ω has been chosen so that frequency falls into the theta range; is a scaling parameter that scales inversely with the time constant of synaptic inhibition; is a second, slower, scaling parameter that has been chosen such that , an assumption that is motivated by biophysical considerations and, in addition, keeps the three time scales (theta rhythm, synaptic inhibition, and excitatory membrane potential) well separate.
We will consider two cases: the oscillator case, defined by , and the excitable case, defined by . The characterizing feature of the oscillator setting is that – subsystem in (1) is an intrinsic oscillator at every stage of a Θ-cycle, i.e., the total current input to EG neuron is always positive. In the excitable case, on the other hand, part of theta oscillation period is such that subsystem of (1) has an attracting quasisteady state, i.e., the total input to the EG neuron is negative or positive depending on Θ-oscillator phase. If , the net input to EG neuron is always negative and the gamma circuit is always silent.
where are Bessel functions of the first kind of order ν. The only solution approaching the left branch of the nullcline parabola for is the one obtained by choosing , thus we pick this value of c. The inverse of function , namely , defines the trajectory of x as a function of y (Fig. 3). Unfortunately, due to its highly nonlinear form, it is impossible to compute directly.
Note that the solution with initial conditions (8), transformed to the coordinates , satisfies the assumptions of Proposition 1. Therefore, estimate (17) holds.
The term in (17) and the following one, of order in , happen to be zero in the theta neuron model (as well as in the QIF model) when there is no excitatory feedback from the EG cell to the theta band oscillator (see the Appendix). The next nonzero term in (17) is then of order , which represents the error with respect to the time at which the true trajectory of the system reaches . The value of δ does not have to be small, on the contrary our approximation works better when δ is such that the trajectory of the system is close to the asymptote , as it is the case for the EG cell spike threshold .
6.1 Second Gamma Spike
6.2 Subsequent Gamma Spikes
where now the extremes in the integrals are chosen to be the times of the first and last gamma spikes (i.e., the times when the EG neuron crosses the SNIC bifurcation respectively from below and above), assuming that these would be approximately symmetric with respect to the Θ cycle.
In this paper, we investigated how a continuous, strong, low frequency (1–10 Hz) modulation determines the spiking properties of a simplified PING oscillator. This work has been particularly motivated by recent investigation on the role of theta-gamma interactions in processing speech signals . Syllabic input are in fact known to possess a quasiperiodic structure matching theta frequency . Within this framework, theta-modulated gamma spikes need to be aligned to the onset and the offset of linguistically relevant chunks . It is then crucial to understand the timing of gamma spikes and the way they are influenced by theta input, since theta is supposed to detect the presence of long timescale syllabic content. It remains to be unveiled whether the scaling we analytically determined here is produced in more realistic models for speech processing  currently under development. Indeed, this result could also be used for other purposes: investigating how theta fluctuations modulate gamma firing in the hippocampus; determining the impact of alpha oscillations on higher frequencies (including gamma), which are thought to carry bottom-up information in visual perception. Indeed timing of first spike is assumed to be particularly relevant in visual cortex, since it is has been shown that it would facilitate the neural encoding of stimuli .
To explore the dynamics of the system, we split the problem into two parameter regimes: In the first, the frequency of gamma spikes is only modulated by theta phase, while in the second the gamma cell would only fire if forced by theta input. In the former regime, by restating the problem in form of a Mathieu differential equation and looking at the first zero of the Mathieu function solving the initial value problem, we were able to find the time to first gamma spike. In the latter, we separate the dynamics into three time scales, one characterizing EG neuron dynamics in absence of any external input, one for theta dynamics, and one for synaptic inhibition, and we approximate the time to first spike by using an extension of the geometric singular perturbation theory based on the application of the blow-up method [46, 48].
Computations align with the intuition (arising from the fact that is a type I neuron) that time to first spike decreases in both cases with coupling strength λ and constant driving current . Interestingly, in the excitable case, we found that time to first spike depends approximately on , which implies that it saturates rapidly as λ grows. As a second notable result, scales as , being the speed of theta cycle. Building on these results, we subsequently computed the time to successive spikes in both regimes, where inhibitory synaptic decay time becomes an important factor. For both regimes were able to compute approximate spike times and predict the exact number of spikes per theta cycle (and instantaneous frequency of firing as a direct consequence) in a range of parameter values that leads to firing within the gamma frequency band.
In the present work, we analyzed a simple system in which coupling was limited to a feedforward theta-gamma connection. It would be a natural next step to extend the analysis to bidirectional coupling by including a feedback from gamma spikes to the Θ oscillator. A second assumption we made in constructing our system stated that the gamma circuit internal delay between excitatory and inhibitory spikes was negligible, meaning that both cells would fire at exactly the same time. To make the model more biologically appealing, one could relax this hypothesis by introducing a synaptic delay after an excitatory spike and study the correspondent system (i.e., a full PING). For relatively short delays, we would expect the results obtained in this paper to hold at least qualitatively. Throughout this paper, we considered gamma to be a simplified PING generator, on the other hand it still remains an open question whether the same characteristics of theta-gamma modulation we explored here would still be found in a different gamma generator, e.g., an Interneuron Network Gamma (ING) network  that can still be implement with Type I neurons as in the case of this work.
We show that the term of order in expansion (17) is zero in our model. The subsequent term, of order is also zero, but we do not include the result here since computations are long and heavy. The interested reader could derive this result from .
which approximates the time to the first spike in the excitable case.
where S stands for the coordinates of the singular point and subscripts indicate the derivatives, i.e., is the first derivative of φ with respect to x, taken at . It is easy to verify that any derivative of φ with respect to x of order n, for n odd, is equal to zero at S. Furthermore, since is constant in system (40), is clearly zero. Hence, .
LF, AH are supported by the European Research Council; AH and BG are partially supported by ANR “Neurobot” and “Dopanic”; BG is supported by CNRS and INSERM and LABEX “IEC”; MK supported by INRIA.
- Buzsaki G, Draguhn A: Neuronal oscillations in cortical networks. Science 2004, 304: 1926–1929. 10.1126/science.1099745View ArticleGoogle Scholar
- Wang XJ: Neurophysiological and computational principles of cortical rhythms in cognition. Physiol Rev 2010, 90: 1195–1268. 10.1152/physrev.00035.2008View ArticleGoogle Scholar
- Womelsdorf T, Schoffelen J, Oostenveld R, Singer W, Desimone R, Engel AK, Fries P: Modulation of neuronal interactions through neuronal synchronization. Science 2007, 316: 1609–1612. 10.1126/science.1139597View ArticleGoogle Scholar
- Young CK, Eggermont JJ: Coupling of mesoscopic brain oscillations: recent advances in analytical and theoretical perspectives. Prog Neurobiol 2009, 89(1):61–78. 10.1016/j.pneurobio.2009.06.002View ArticleGoogle Scholar
- Schoffelen J, Oostenveld R, Fries P: Neuronal coherence as a mechanism of effective corticospinal interaction. Sci Signal Transduct Knowl Environ 2005, 308(5718):111.Google Scholar
- Huerta PT, Lisman JE: Bidirectional synaptic plasticity induced by a single burst during cholinergic theta oscillation in CA1 in vitro. Neuron 1995, 15(5):1053–1063. 10.1016/0896-6273(95)90094-2View ArticleGoogle Scholar
- Fell J, Klaver P, Lehnertz K, Grunwald T, Schaller C, Elger C, Fernández G: Human memory formation is accompanied by rhinal-hippocampal coupling and decoupling. Nat Neurosci 2001, 4: 1259–1264. 10.1038/nn759View ArticleGoogle Scholar
- Jensen O, Kaiser J, Lachaux JP: Human gamma-frequency oscillations associated with attention and memory. Trends Neurosci 2007, 30(7):317–324. 10.1016/j.tins.2007.05.001View ArticleGoogle Scholar
- Fries P, Reynolds J, Rorie A, Desimone R: Modulation of oscillatory neuronal synchronization by selective visual attention. Science 2001, 291(5508):1560–1563. 10.1126/science.1055465View ArticleGoogle Scholar
- Gray CM, König P, Engel A, Singer W: Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 1989, 338(6213):334–337. 10.1038/338334a0View ArticleGoogle Scholar
- Bragin A, Jandó G, Nádasdy Z, Hetke J, Wise K, Buzsáki G: Gamma (40–100 Hz) oscillation in the hippocampus of the behaving rat. J Neurosci 1995, 15: 47–60.Google Scholar
- Jensen O, Colgin LL: Cross-frequency coupling between neuronal oscillations. Trends Cogn Sci 2007, 11(7):267–269. 10.1016/j.tics.2007.05.003View ArticleGoogle Scholar
- Arnal LH, Morillon B, Kell CA, Giraud AL: Dual neural routing of visual facilitation in speech processing. J Neurosci 2009, 29(43):13445–13453. 10.1523/JNEUROSCI.3194-09.2009View ArticleGoogle Scholar
- Lakatos P, Shah AS, Knuth KH, Ulbert I, Karmos G, Schroeder CE: An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex. J Neurophysiol 2005, 94(3):1904–1911. 10.1152/jn.00263.2005View ArticleGoogle Scholar
- Canolty RT, Edwards E, Dalal SS, Soltani M, Nagarajan SS, Kirsch HE, Berger MS, Barbaro NM, Knight RT: High gamma power is phase-locked to theta oscillations in human neocortex. Science 2006, 313(5793):1626. 10.1126/science.1128115View ArticleGoogle Scholar
- Morillon B, Liégeois-Chauvel C, Arnal LH, Bénar CG, Giraud AL: Asymmetric function of theta and gamma activity in syllable processing: an intra-cortical study. Front Psychol 2012., 3: Article ID 248 Article ID 248Google Scholar
- Belluscio MA, Mizuseki K, Schmidt R, Kempter R, Buzsaki G: Cross-frequency phase-phase coupling between θ and γ oscillations in the hippocampus. J Neurosci 2012, 32(2):423–435. 10.1523/JNEUROSCI.4122-11.2012View ArticleGoogle Scholar
- Palva S, Palva JM: The functional roles of alpha-band phase synchronization in local and large-scale cortical networks. Front Psychol 2011., 2: Article ID 204 Article ID 204Google Scholar
- Scheffer-Teixeira R, Belchior H, Caixeta FV, Souza BC, Ribeiro S, Tort ABL: Theta phase modulates multiple layer-specific oscillations in the CA1 region. Cereb Cortex 2012, 22(10):2404–2414. 10.1093/cercor/bhr319View ArticleGoogle Scholar
- Wulff P, Ponomarenko AA, Bartos M, Korotkova TM, Fuchs EC, Bähner F, Both M, Tort ABL, Kopell NJ, Wisden W, Monyer H: Hippocampal theta rhythm and its coupling with gamma oscillations require fast inhibition onto parvalbumin-positive interneurons. Proc Natl Acad Sci USA 2009, 106(9):3561–3566. 10.1073/pnas.0813176106View ArticleGoogle Scholar
- Buzsaki G, Buhl DL, Harris KD, Csicsvari J, Czéh B, Morozov A: Hippocampal network patterns of activity in the mouse. Math Eng Ind 2003, 116: 201–211.Google Scholar
- Ghitza O: Linking speech perception and neurophysiology: speech decoding guided by cascaded oscillators locked to the input rhythm. Front Psychol 2011., 2: Article ID 130 Article ID 130Google Scholar
- Giraud AL, Poeppel D: Cortical oscillations and speech processing: emerging computational principles and operations. Nat Neurosci 2012, 15(4):511–517. 10.1038/nn.3063View ArticleGoogle Scholar
- Shamir M, Ghitza O, Epstein S, Kopell NJ: Representation of time-varying stimuli by a network exhibiting oscillations on a faster time scale. PLoS Comput Biol 2009., 5(5): Article ID e1000370 Article ID e1000370Google Scholar
- Bressloff PC, Coombes S: Dynamics of strongly-coupled spiking neurons. Neural Comput 2000, 12: 91–129. 10.1162/089976600300015907View ArticleGoogle Scholar
- Brunel N, Hakim V: Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Comput 1999, 11(7):1621–1671. 10.1162/089976699300016179View ArticleGoogle Scholar
- Ermentrout GB, Chow CC: Modeling neural oscillations. Physiol Behav 2002, 77(4–5):629–633.View ArticleGoogle Scholar
- Ko TW, Ermentrout G: Phase-response curves of coupled oscillators. Phys Rev E 2009, 79: 1–6.MathSciNetView ArticleGoogle Scholar
- Kopell N, Ermentrout G: Mechanisms of phase-locking and frequency control in pairs of coupled neural oscillators. 2. In Handbook of Dynamical Systems. Elsevier, Amsterdam; 2002:3–54.Google Scholar
- Ostojic S, Brunel N, Hakim V: Synchronization properties of networks of electrically coupled neurons in the presence of noise and heterogeneities. J Comput Neurosci 2009, 26(3):369–392. 10.1007/s10827-008-0117-3MathSciNetView ArticleGoogle Scholar
- Tiesinga PH, Sejnowski TJ: Mechanisms for phase shifting in cortical networks and their role in communication through coherence. Front Human Neurosci 2010, 4: 196.View ArticleGoogle Scholar
- Kilpatrick ZP, Ermentrout B: Sparse gamma rhythms arising through clustering in adapting neuronal networks. PLoS Comput Biol 2011., 7(11): Article ID e1002281 Article ID e1002281Google Scholar
- Tiesinga P, Sejnowski TJ: Cortical enlightenment: are attentional gamma oscillations driven by ING or PING? Neuron 2009, 63(6):727–732. 10.1016/j.neuron.2009.09.009View ArticleGoogle Scholar
- Ermentrout G, Kopell N: Parabolic bursting in an excitable system coupled with a slow oscillation. SIAM J Appl Math 1986, 46(2):233–253. 10.1137/0146017MathSciNetView ArticleGoogle Scholar
- Lewis TJ, Rinzel J: Dynamics of spiking neurons connected by both inhibitory and electrical coupling. SIAM J Appl Math 2003, 14: 283–309.Google Scholar
- Yu N, Kuske R, Li YX: Stochastic phase dynamics and noise-induced mixed-mode oscillations in coupled oscillators. Chaos 2008., 18: Article ID 015112 Article ID 015112Google Scholar
- Ledoux E, Brunel N: Dynamics of networks of excitatory and inhibitory neurons in response to time-dependent inputs. Front Comput Neurosci 2011., 5: Article ID 25 Article ID 25Google Scholar
- Vierling-Claassen D, Kopell N: The dynamics of a periodically forced cortical microcircuit, with an application to schizophrenia. SIAM J Appl Dyn Syst 2009, 8(2):710. 10.1137/080738052MathSciNetView ArticleGoogle Scholar
- Tort ABL, Rotstein HG, Dugladze T, Gloveli T, Kopell NJ: On the formation of gamma-coherent cell assemblies by oriens lacunosum-moleculare interneurons in the hippocampus. Proc Natl Acad Sci USA 2007, 104(33):13490–13495. 10.1073/pnas.0705708104View ArticleGoogle Scholar
- Börgers C, Kopell NJ: Effects of noisy drive on rhythms in networks of excitatory and inhibitory neurons. Neural Comput 2005, 17(3):557–608. 10.1162/0899766053019908MathSciNetView ArticleGoogle Scholar
- Kopell NJ, Börgers C, Pervouchine D, Malerba P, Tort ABL: Gamma and theta rhythms in biophysical models of hippocampal circuits. In Hippocampal Microcircuits. Springer, New York; 2010:423–457.View ArticleGoogle Scholar
- Roopun AK, Kramer M, Carracedo LM, Kaiser M, Davies CH, Traub RD, Kopell NJ, Whittington M: Temporal interactions between cortical rhythms. Front Neurosci 2008, 2(2):145–154. 10.3389/neuro.01.034.2008View ArticleGoogle Scholar
- Schroeder CE, Lakatos P: Low-frequency neuronal oscillations as instruments of sensory selection. Trends Neurosci 2009, 32: 9–18. 10.1016/j.tins.2008.09.012View ArticleGoogle Scholar
- Ruby L: Applications of the Mathieu equation. Am J Phys 1996, 64: 39. 10.1119/1.18290MathSciNetView ArticleGoogle Scholar
- Fenichel N: Geometric singular perturbation theory. J Differ Equ 1979, 31: 53–98. 10.1016/0022-0396(79)90152-9MathSciNetView ArticleGoogle Scholar
- van Gils SA, Krupa M, Szmolyan P: Asymptotic expansions using blow-up. Z Angew Math Phys 2005, 56(3):369–397. 10.1007/s00033-004-1021-yMathSciNetView ArticleGoogle Scholar
- Krupa M, Szmolyan P: Relaxation oscillations and canard explosion. J Differ Equ 2001, 174: 312–368. 10.1006/jdeq.2000.3929MathSciNetView ArticleGoogle Scholar
- Krupa M, Szmolyan P: Extending geometric singular perturbation theory to nonhyperbolic points—fold and canard points in two dimensions. SIAM J Math Anal 2001, 33: 286–314. 10.1137/S0036141099360919MathSciNetView ArticleGoogle Scholar
- Ermentrout B: Type I membranes, phase resetting curves, and synchrony. Neural Comput 1996, 8: 979–1001. 10.1162/neco.19126.96.36.1999View ArticleGoogle Scholar
- Tonnelier A, Belmabrouk H, Martinez D: Event-driven simulations of nonlinear integrate-and-fire neurons. Neural Comput 2007, 19(12):3226–3238. 10.1162/neco.2007.19.12.3226MathSciNetView ArticleGoogle Scholar
- Mishchenko EF, Rozov NK: Differential Equations with Small Parameters and Relaxation Oscillations. Plenum Press, New York; 1980.View ArticleGoogle Scholar
- Hyafil A, Gutkin B, Giraud AL: A theoretical exploration of speech/neural oscillation alignment for speech parsing. Front. Hum. Neurosci. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI) 2011.Google Scholar
- Thorpe S, Delorme A, Van Rullen R: Spike-based strategies for rapid processing. Neural Netw 2001, 14(6–7):715–725.View ArticleGoogle Scholar
- Wang XJ, Buzsaki G: Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. J Neurosci 1996, 16: 6402–6413.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.