Criticality and chaos in auditory and vestibular sensing | Scientific Reports - Nature.com - Fresh Start Speech Therapy Service


Criticality and chaos in auditory and vestibular sensing | Scientific Reports – Nature.com

Summary

The auditory and vestibular methods exhibit outstanding sensitivity of detection, responding to deflections on the order of angstroms, even within the presence of organic noise. The auditory system displays excessive temporal acuity and frequency selectivity, permitting us to make sense of the acoustic world round us. Because the acoustic indicators of curiosity span many orders of magnitude in each amplitude and frequency, this method depends closely on nonlinearities and power-law scaling. The vestibular system, which detects ground-borne vibrations and creates the sense of stability, displays extremely delicate, broadband detection. It likewise requires excessive temporal acuity in order to permit us to keep up stability whereas in movement. The conduct of those sensory methods has been extensively studied within the context of dynamical methods idea, with many empirical phenomena described by essential dynamics. Different phenomena have been defined by methods within the chaotic regime, the place weak perturbations drastically affect the long run state of the system. Utilizing a Hopf oscillator as a easy numerical mannequin for a sensory factor in these methods, we discover the intersection of the 2 forms of dynamical phenomena. We establish the relative tradeoffs between completely different detection metrics, and suggest that, for each forms of sensory methods, the instabilities giving rise to chaotic dynamics enhance sign detection.

Introduction

Auditory and vestibular methods carry out duties which might be essential for the survival of an animal, enabling it to navigate in house, detect indicators from predators or prey, establish and appeal to potential mates, and talk with members of the identical species. To attain these duties, the sensory system should detect extraordinarily weak indicators, extract them from noisy environments, distinguish tones of intently spaced frequencies, and exactly parse temporal info. Particularly, close to our threshold of listening to, we’re capable of detect displacements of the eardrum smaller than the width of the hydrogen atom1. This detection happens within the presence of inner thermal fluctuations of equal or greater magnitude. Additional, the temporal decision of people usually reaches 10 ms2,3, enabling sound localization by way of interaural time variations4. Lastly, we’re capable of resolve tones that differ in frequency by solely a fraction of a %5. In parallel, the auditory system achieves immense dynamic vary in each amplitude and frequency of acoustic detection. We’re capable of detect sounds that span over 12 orders of magnitude in depth and three orders of magnitude in frequency. These broad ranges are mirrored in our logarithmic decibel scale for sound depth and logarithmic spacing of musical intervals.

The outstanding options of the auditory system rely closely on two components: energetic amplification and nonlinear response. First, the system has been proven to violate the fluctuation dissipation theorem, indicating that it can’t be ruled by equilibrium statistical mechanics6. Huge quantities of empirical proof obtained in vivo display that the internal ear expends power to amplify indicators7. Second, compressive nonlinearities within the response to exterior stimuli allow the intensive dynamic vary, whereas sustaining sensitivity to weak indicators6,8. These nonlinearities have been detected in any respect scales measured, from particular person sensory cells to in vivo phenomena often known as phantom tones9,10,11. How the auditory system makes use of power expenditure and nonlinearities to realize its outstanding detection traits, nonetheless, stays unknown after greater than 7 a long time of analysis12,13,14.

The theoretical framework for auditory detection that was developed over the previous 20 years relies on the notion of a dynamical system poised close to criticality15, on the verge of autonomous oscillation. The fashions apply the traditional type equation for the supercritical Hopf bifurcation to explain the auditory system, and elegantly seize the mechanical sensitivity, frequency selectivity, and amplitude-compressive response, thus reproducing a broad vary of experimental outcomes16,17. Within the neighborhood of the bifurcation, the system’s sensitivity to exterior indicators will increase, whereas the frequency selectivity sharpens, thus pointing to criticality because the optimum regime for sign detection18.

Nonetheless, whereas proximity to a bifurcation yields many benefits, the outline incorporates an innate constraint: at criticality, the system turns into infinitely sluggish, with transient occasions diverging because of essential slowing down. This sluggish conduct poses an undesirable trade-off between the sensitivity of a detector and its pace, and is inconsistent with the excessive temporal acuity exhibited by our auditory system. Moreover, on the essential level, the system may be very delicate to the results of stochastic fluctuations, which limits a few of the benefits noticed within the deterministic fashions. As noise is a ubiquitous part of any organic system, and its results particularly close to criticality will not be negligible, this constraint limits the benefits of tuning a system close to a bifurcation level.

An alternate theoretical framework, developed to reconcile the requirement for prime sensitivity of detection with the necessity for a fast temporal response, relies on the notion of chaotic dynamics19. When a system is poised in a chaotic regime, even infinitesimally weak exterior perturbations set off giant modifications within the subsequent trajectory, thus yielding excessive sensitivity. Because of this exponential divergence, the system additionally responds and resets quickly. Therefore, a chaotic system avoids the inherent tradeoff noticed with criticality.

In a previous research, we demonstrated experimentally the presence of a chaotic attractor within the innate and pushed oscillations exhibited by sensory hair cells in vitro20. Utilizing mathematical strategies from dynamical methods literature, we confirmed that the oscillator incorporates a deterministic part and isn’t utterly dominated by organic noise and different stochastic processes. These mathematical strategies have additionally been used to point out the presence of chaos in experimental recordings of human otoacoustic emissions21.

We word that chaos is usually thought of a dangerous factor in utilized arithmetic literature, because it limits management and predictability. Nonetheless, it has been proposed to play a probably useful function in sure organic methods, because it enhances their dynamical complexity22,23. Additional, utilizing a numerical mannequin and experimental recordings, we confirmed chaos to be useful to particular person hair cells, because the instabilities from which chaos arises improve the sensitivity and temporal decision of the response19. As chaotic regimes can come up within the presence of three or extra levels of freedom, we predict that many extra examples of dwelling methods using chaotic dynamics shall be uncovered sooner or later. This phenomenon is harking back to stochastic resonance, a mechanism exhibited by excitable methods, the place sign detection is surprisingly improved with the addition of noise. Stochastic resonance has been proven to be current within the dynamics of Hopf oscillators24, in addition to experimental measurements from the bullfrog internal ear25.

Within the current work, we purpose to match the relative benefits of criticality versus the instabilities that trigger chaos, in a theoretical description of auditory and vestibular detection. Specifically, we discover the interface of those two theoretical fashions: a system poised close to the supercritical Hopf bifurcation and one poised within the chaotic regime, in addition to the continuum describing transitions between the regimes. To evaluate individually every of the important thing options exhibited by these sensory methods, we characterize the sensitivity, frequency selectivity, temporal acuity, and power-law amplitude response of a Hopf oscillator, close to and much from criticality, within the presence and absence of chaos. As stochastic fluctuations play a non-negligible function in dynamical methods, we discover the relative tradeoffs between these detection traits within the presence of noise. We then mix these impartial metrics to suggest a easy conceptual framework for auditory and vestibular detection.

Hopf bifurcation and significant slowing down

The internal ear of vertebrates incorporates plenty of finish organs specializing in both auditory or vestibular detection, the latter together with each translational and rotational motion. Whereas the pathways by which exterior indicators attain the internal ear differ, they finally end in mechanical vibrations of inner constructions; therefore, the 2 sensory methods exhibit many options in frequent. Conversion of mechanical power of a sound, vibration, or acceleration into electrical power within the type of ionic currents is carried out by specialised, sensory hair cells of each the auditory and vestibular methods. The hair cell will get its identify from the rod-like, inter-connected stereocilia that protrude from the apical floor, that are collectively named the hair bundle. An incoming stimulus pivots the stereocilia and modulates the open chance of the transduction channels embedded within the suggestions of the stereocilia26,27. In a number of species, these hair bundles have been proven to exhibit energetic limit-cycle oscillations within the absence of utilized stimulus6,28,29,30. Although the function of those spontaneous hair-bundle oscillations in vivo has but to be established, they function an experimental probe for finding out this energetic system, as they result in sub-nanometer thresholds in vitro31,32.

The Hopf oscillator has been extensively used for modeling and understanding the phenomenon of energetic, spontaneous hair-bundle oscillations, in addition to extra world options of the auditory and vestibular methods18,33. When the system is poised on the verge of instability (close to the Hopf bifurcation), it turns into extraordinarily delicate to small perturbations. Within the absence of noise, the amplitude achieve of the response diverges because the system approaches criticality. The mannequin was proven to seize energetic amplification and power-law amplitude response noticed each in vivo and in vitro on plenty of completely different species5.

A Hopf oscillator is described by time-dependent complicated variable, z(t), which is ruled by a standard type equation, which in its easiest model, takes the shape:

$$start{aligned} frac{dz}{dt} = (mu + iomega _0)z – |z|^2z + F(t) + eta (t) finish{aligned}$$
(1)

the place (mu) and (omega _0) are the management parameter and attribute frequency of the detector, respectively. F(t) represents the exterior forcing on the system, whereas (eta (t)) is a stochastic variable, representing thermal noise. This variable is complicated, with impartial actual and imaginary elements, each of which have statistics of Gaussian white noise: (langle eta (t)rangle = 0), (langle eta (t)eta (t’)rangle = 0), and (langle eta (t)bar{eta }(t’)rangle = 4Ddelta (t-t’)), the place (bar{eta }) is the complicated conjugate of (eta), and D defines the noise energy.

Within the absence of forcing and noise ((F(t) = D = 0)), the system might be extra simply understood in polar coordinates, by letting (z(t) = r(t)e^{itheta (t)}), thereby separating the complicated variable into two actual variables. This leads to the pair of equations,

$$start{aligned} frac{dr}{dt} = mu r – r^3 quad textual content{and} quad frac{dtheta }{dt} = omega _0, finish{aligned}$$
(2)

which describe the amplitude and section dynamics of the system, respectively. Discover that the instantaneous frequency (frac{dtheta }{dt}) is fixed, having no dependence on the oscillator’s amplitude, r(t). This defines an isochronous oscillator.

The amplitude dynamics are decided by the management parameter, (mu). For (mu < 0), the system shows a steady fastened level with rising native stability for extra unfavorable values of the management parameter. Because the management parameter approaches the essential level at (mu = 0), the system loses its native stability on the origin, and steady limit-cycle oscillations emerge. For (mu > 0), as this parameter will increase, the spontaneous oscillations develop bigger, and the native stability of the restrict cycle will increase. Exactly on the essential level, the amplitude achieve of the system diverges within the absence of noise, as an infinitesimal perturbation causes large-amplitude displacements18 (Fig. S1). For each constructive and unfavorable values of the management parameter, the amplitude returns to its steady-state exponentially, with attribute time scale proportional to (1/mu) (see Supplementary Materials). Within the neighborhood of the bifurcation, the linear time period turns into vanishingly small, and the system returns to steady-state very slowly, with perturbations diminishing in response to a power-law. Because the management parameter approaches the Hopf bifurcation, this time scale diverges with out sure at (mu =0), a dynamical methods phenomenon often known as essential slowing down (Fig. S1).

Determine 1

(A) Lyapunov exponent calculated numerically with (beta =5), for a spread of management parameters. (B) Lyapunov exponent calculated numerically with (mu =1), for a spread of levels of nonisochronicity. For (A, B), the black curves correspond to the analytic approximations of the Lyapunov exponent. (C) Lyapunov exponent calculated numerically all through the parameter house. The dotted strains correspond to the cross-sections plotted in (A, B). For all panels, (D=0.1).

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Nonisochronicity and chaos

In a number of research of auditory and vestibular methods, a extra normal model of the Hopf oscillator was thought of19,34. The equation takes the shape

$$start{aligned} frac{dz}{dt} = (mu + iomega _0)z – (alpha + ibeta )|z|^2z + F(t) + eta (t) , finish{aligned}$$
(3)

the place (alpha) and (beta) are launched to characterize the nonlinearity of the system, whereas all the opposite parameters carry the identical which means as within the isochronous case beforehand described. Within the absence of stimulus and noise ((F(t) = D = 0)), this equation might be written in polar coordinates as

$$start{aligned} frac{dr}{dt} = mu r – alpha r^3 quad textual content{and} quad frac{dtheta }{dt} = omega _0 – beta r^2 finish{aligned}$$
(4)

For (mu > 0), a steady restrict cycle exists at radius (r_0 = sqrt{frac{mu }{alpha }}). The frequency at this restrict cycle is (Omega _0 = omega _0 – beta r_0^2 = omega _0 – beta mu / alpha), the place (omega _0) is the frequency on the Hopf bifurcation. This extra normal description reduces to the normal, isochronous type when (alpha = 1) and (beta = 0). We limit our evaluation to methods with (alpha = 1) and differ (beta) to regulate the extent of nonisochronicity. This new parameter causes the frequency of oscillation to rely on the amplitude of oscillation. Experimental recordings obtained from in vitro preparations of hair cells have proven signatures of nonisochronicity in spontaneously oscillating hair bundles. The coupling between frequency and amplitude might be seen by way of experimental manipulations of the innate restrict cycle, similar to imposing giant deflections on the hair bundle35, or by adjusting the calcium focus of the encircling resolution19.

By coupling the radial and section levels of freedom, the presence of nonisochronicity ((beta ne 0)) introduces important complexity to the dynamics of this method. It distorts the Lorentzian form of the frequency response curve36 and may even produce branching of this curve, discontinuities, and bistability for a spread of frequencies, in addition to hysteretic conduct in response to frequency sweeps37. The presence of nonisochronicity additionally makes the system inclined to chaotic dynamics. Chaos in dynamical methods is characterised by excessive sensitivity to preliminary situations and exponential divergence of neighboring trajectories. Within the nonisochronous Hopf oscillator, chaos can come up from sinusoidal or impulsive exterior forcing, in addition to methods pushed purely by stochastic white noise19,38.

The speed of divergence of neighboring trajectories is predicted to observe (|Delta z(t)| propto e^{lambda t}), the place (Delta z(t)) is the Euclidean distance between two close by phase-space trajectories, and (lambda) is the Lyapunov exponent. Chaotic methods are sometimes outlined by (lambda > 0). For quiescent, non-chaotic methods, the Lyapunov exponent is unfavorable and characterizes the system’s stability, or the speed at which the system returns to equilibrium following a perturbation. For a Hopf oscillator with (mu < 0) and no forcing or noise, the Lyapunov exponent might be discovered by increasing across the steady fastened level (see Supplementary Materials). One finds that on this easy case, (lambda = mu), which signifies that the system turns into extra steady on the origin for extra unfavorable values of (mu).

Within the absence of noise, because the management parameter crosses from the quiescent to the oscillatory regime, the Lyapunov exponent will increase to zero and stays there even for ((mu > 0)). Since perturbations within the section neither diverge nor converge, trajectories are neutrally steady alongside the angular path of the restrict cycle. Within the presence of noise, analytic approximations typically turns into intractable, however numerical simulations yield values of the Lyapunov exponent that may be constructive, unfavorable, or zero.

For a loud nonisochronous Hopf oscillator, an analytic approximation might be made within the regime of sufficiently steady restrict cycles. On this regime, the Lyapunov exponent reveals a easy dependence on each the management and nonisochronicity parameters19,

$$start{aligned} lambda approx fracbeta {mu } . finish{aligned}$$
(5)

This approximation is especially helpful in weakly chaotic regimes, the place numerical simulations are computationally costly. We present the robustness of this approximation for sufficiently steady restrict cycles, in addition to the easier approximation for quiescent methods (Fig. 1A-B). The analytic approximation nonetheless breaks down because the system approaches criticality from the oscillatory aspect, as a result of the idea of a sufficiently steady restrict cycle is not legitimate. We due to this fact present a map of the Lyapunov exponent calculated numerically all through the parameter house (Fig. 1C). These calculations serve to point out the extent of chaos because the system crosses the Hopf bifurcation, a regime not explored analytically. Additional, this mapping illustrates the connection between the Lyapunov exponent and the extent of nonisochronicity. Observe, nonetheless, that the extent of chaos is determined by the noise energy, D, and therefore the precise map modifications if the noise energy is diverse. Within the following sections, we assess the affect of the management parameter, (mu), the nonisochronicity parameter, (beta), and the noise energy, D.

Determine 2
figure 2

(A, C, E) Sensitivity metrics for the essential system ((mu =beta = 0), black crammed factors) and chaotic system ((mu =1, beta = 5), crimson crammed factors) for a spread of noise strengths. Dotted horizontal strains correspond to the worth of every metric within the deterministic restrict. In (C, E), open factors correspond to switch entropy within the reverse path (response to stimulus) and function controls. The gray, shaded areas estimate the organic noise stage skilled by hair bundles (1–5% of the sign amplitude35). The three stimulus varieties are illustrated above every column: pure tone, frequency modulation (FM), and amplitude modulation (AM). (B, D, F) Heatmaps of the three measures all through the parameter house with (D=10^{-3}). Cross-sections alongside the coloured, dotted strains are proven beneath.

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Determine 3
figure 3

(A) Responses of the essential ((mu =beta = 0), black curve) and chaotic ((mu =1, beta = 5), crimson curve) methods to a step stimulus (illustrated by the blue dashed curve). Response curves symbolize averages over 64 simulations, every with completely different preliminary situations and realizations of noise. The dotted strains point out the imply amplitude previous to the step onset. (B) Response occasions (open squares) and return occasions (crammed circles) of the essential (black) and chaotic (crimson) methods for a number of ranges of noise. The return time of the essential system quickly diverges because the noise is diminished. Return occasions longer than 100 cycles will not be plotted. The gray, shaded areas estimate the organic noise stage skilled by hair cells. (C) Similar information as (B) however zoomed in and on a linear scale. (D–F) Heatmaps of the the pace of response, pace of return, and slower of the 2 measures. For each mixture of parameters, the return time exceeded the response time ((tau _{off} > tau _{on})). For all heatmaps, (D=10^{-3}).

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Mechanical sensitivity and data switch

Close to the Hopf bifurcation, a noiseless system shows immense sensitivity, with the amplitude achieve of the response diverging exactly on the bifurcation18. Nonetheless, this compliance additionally makes the system inclined to stochastic fluctuations. Within the oscillatory regime, the detector turns into extra immune to noise, but in addition tougher to entrain by exterior indicators. Nonisochronicity can help detectors in synchronizing to exterior indicators; nonetheless, this impact can likewise improve susceptibility to exterior noise and off-resonance stimulus frequencies. We therefore decide the sensitivity of the Hopf oscillator all through the (mu beta)-plane and establish the popular regimes for a number of forms of stimulus.

We first contemplate a weak, single-tone, on-resonance stimulus within the presence of noise. We make use of the linear response perform, (chi (Omega _0)), to characterize the sensitivity of the system (see Numerical Strategies). For methods that exhibit autonomous oscillations, this measure displays a spurious non-zero worth if the response doesn’t synchronize to the sign. To keep away from this problem, we make sure that solely the phase-locked part is included within the calculation. We do that by averaging the responses over many methods, every ready with completely different preliminary phases, thereby averaging out any oscillatory part that doesn’t synchronize to the stimulus. It has beforehand been proven that the isochronous Hopf oscillator detects this sign greatest when poised as far into the oscillatory regime as attainable39. Our outcomes are according to this discovering, with even additional enchancment when the system is weakly nonisochronous (Fig. 2). Additional, we present that detectors on this regime outperform these within the essential regime for all ranges of noise thought of.

Subsequent, we contemplate stochastic modulations within the amplitude (AM) and frequency (FM) of this sign (see Numerical Strategies). Not like the pure-tone stimulus, these indicators carry info of their modulations. We due to this fact consider the detector not solely as a mechanical resonator, but in addition as an information-theoretic receiver. We make use of switch entropy because the measure of data captured by the receiver40. This measure is especially helpful, because it carries no assumptions about which options of the exterior sign are essential. As an alternative, it straight measures the quantity of data transmitted from one course of to a different, and may even be used to ascertain causality between two processes. For all parameter ranges examined and for each forms of modulation, detectors which might be poised within the oscillatory, nonisochronous regime seize essentially the most info from the utilized stimulus. We additional explored the sensitivity of this detection metric to stochastic fluctuations and located the nonisochronous regime to yield extra robustness than the essential, isochronous regime (Fig. 2).

Determine 4
figure 4

(A) Energy spectral density of the system by way of the parameter house, in response to white noise stimulus. All curves are normalized to their peak values and plotted on a linear scale. (B) Heatmap of the standard issue as measured from the response curves. (C) Heatmap of the edge bandwidth, indicating the frequency vary for which the Fourier-component amplitudes of the response exceed 0.1. For all panels, (D=0.01). Cross-sections of the heatmaps are proven on the proper.

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Temporal acuity

Excessive temporal acuity is important for a sensory system to be aware of temporary indicators. Additional, localization of sound by vertebrates depends on interaural time variations as small as a couple of tens of microseconds, which correspond to temporal variations of only a fraction of a single stimulus cycle. When such small variations are biologically significant, the system’s response to a stimulus and its subsequent return to regular state should happen quickly. To characterize this temporal acuity, we apply a step stimulus and measure the time the system requires to achieve its steady-state response, in addition to the time it takes to return to its unstimulated regular state after cessation of the sign.

We beforehand discovered that nonisochronicity enormously will increase the pace of response to a step stimulus38. These outcomes are according to experimental measurements that demonstrated a correlation between the pace of response and the extent of chaos, quantified by the Kolmogorov entropy19. Within the current work, we prolong our evaluation to variations within the management parameter and decide the temporal acuity of the Hopf oscillator within the (mu beta)-plane. We apply a step stimulus to the detector (Fig. 3A), averaging over many simulations with completely different preliminary situations and realizations of noise. This technique of calculating the imply response averages out the stochastic fluctuations, in addition to any autonomous oscillations that might in any other case obscure sluggish modulations to the mean-field response.

We outline the response time ((tau _{on})) to be the time it takes the system to settle to and stay inside 4 normal deviations of its steady-state imply worth following the onset of the step stimulus. Likewise, we outline the return time ((tau _{off})) to be the time it takes the imply response of the system to develop into indistinguishable (inside an ordinary deviation) from its worth previous to the stimulus. We contemplate the limiting issue ((tau _{max})) to be the utmost of the 2 time constants. For all parameters examined, the return time was the limiting issue (Fig. 3D-F). For simplicity, we due to this fact drop the subscripts and let (tau = tau _{max} = tau _{off}).

Stochastic fluctuations not solely obscure the mean-field response, however also can distort it. Because the noise stage will increase, so does the typical amplitude of the system. This improve within the baseline amplitude reduces the space traveled by the returning imply response, successfully rising the pace ((frac{1}{tau })) in response to our definition. We measure the pace of response over 5 orders of magnitude in noise energy and discover that the oscillatory, nonisochronous system is quicker to return to the baseline than the essential system (Fig. 3B-C). For terribly giant ranges of noise, the results of essential slowing down are eliminated. Nonetheless, the degrees of noise adequate to equalize the pace of the 2 forms of methods are so giant as to result in an enormous discount within the sensitivity (Fig. 2A, C, and E).

Determine 5
figure 5

Response amplitude from a pure-tone stimulus for on-resonance (A) and detuned (B, C) stimulus frequencies as indicated in every panel. Blue, orange, and crimson curves correspond to (beta =0), 2, and 5, respectively. Black dashed strains point out linear progress, whereas pink dashed strains point out power-law progress with (|z(omega )| propto F^{frac{1}{3}}). For (A–C), (mu =1) and (D=0). (D) Illustration of how the dynamic vary, (gamma), is calculated. (E) Dynamic vary as a perform of (beta) for a number of fastened values of (mu). Strong curves correspond to the analytic calculation, whereas circles and crosses correspond to simulations with (D=0) and (D=0.001), respectively. (F) Heatmap of the dynamic vary all through parameter house, calculated from numerical simulations with (D=0.001). For (E, F), we use a detuning of (omega = 1.01Omega_0).

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Frequency selectivity and broadband detection

Though chaos could make dynamical methods delicate to small perturbations and exterior indicators, these methods are likely to synchronize to a broad vary of frequencies. This can be a useful impact for broadband detectors, similar to vestibular methods, as it will improve the power transmitted to the system and sure end in decrease thresholds of detection. Nonetheless, for frequency selective detectors, like these of auditory methods, this impact could also be dangerous.

We stimulate the Hopf oscillator with additive Gaussian white noise by adjusting D and measure the ability spectrum of the response all through the parameter house (Fig. 4A). As these curves point out how sharply the system filters the white noise, we use them to characterize the frequency selectivity41. We regulate (beta) and (omega _0) collectively in order to maintain the limit-cycle frequency fastened at (Omega _0 = omega _0 – frac{beta mu }{alpha } = 1) within the deterministic restrict. We then introduce stochastic white noise into the simulations and calculate the response curves. We observe that the frequency selectivity will increase with rising (mu) and reducing (|beta |). Close to the Hopf bifurcation, we see that the resonance frequency is determined by (beta). It is a consequence of the noise altering the steady-state amplitude, and thereby the frequency, on this compliant regime.

To characterize the frequency selectivity of the Hopf oscillator, we make use of the standard issue of the response by estimating the total width at half the utmost ((Delta f)) of the response curves. The unitless high quality issue is outlined as (Q = frac{f_0}{Delta f}), the place (f_0) is the height frequency. As anticipated, essentially the most frequency selective parameter regime happens at excessive values of (mu) and low values of (|beta |) (Fig. 4B). The standard issue is helpful for characterizing the frequency selectivity of a single-frequency or narrowband detector.

As some auditory and most vestibular methods are answerable for detecting a broader vary of frequencies, we make the most of the edge bandwidth (BW) for characterizing multi-frequency or broadband detection, a metric advised in39. We estimate this measure by taking the vary of frequencies whose Fourier elements have magnitudes exceeding a given threshold. We select the edge to be 0.1, which corresponds to roughly an element of 10 above the noise flooring of the essential system. The edge bandwidth will increase with rising (mu), because the power from the spontaneous oscillations amplifies the sign (Fig. 4C). Additional, the edge bandwidth initially will increase with rising (|beta |) as a result of broadening of the frequency response curves. Nonetheless, the BW then diminishes for very giant values of (|beta |), because the power turns into so unfold out in frequency house that few elements exceed the edge.

Energy-law scaling of response

The human auditory system can detect a spread of stimulus amplitudes spanning over 6 orders of magnitude in stress. This course of depends on nonlinearities so as to each amplify weak sounds and attenuate loud sounds, thereby defending the system from harm. These nonlinearities have been measured in vivo by way of otoacoustic emissions and thru laser measurements of basilar membrane movement. Additional, the attenuation of huge amplitudes has been noticed in vitro on energetic hair bundles16. At weak forcing, hair cells show a linear response; because the stimulus amplitudes improve, the amplitude response scales as (F^frac{1}{3}). The linear and 1/3-power-law responses are reproduced properly by the Hopf oscillator, as both the linear or the cubic time period dominates in numerous regimes. Close to criticality, the vary over which the ability regulation is noticed will increase.

We now have demonstrated that the nonisochronicity parameter, (beta), improves the sensitivity to weak indicators. We right here present that this parameter additionally causes stronger attenuation of the response at giant stimulus amplitudes. This elevated attenuation might be understood by calculating the response within the strong-forcing restrict, the place the cubic time period dominates (see Supplementary Materials). On this restrict, the response amplitude is scaled by (frac{1}{(alpha ^2 + beta ^2)^frac{1}{6}}). The mixture of those two results yields a rise within the vary of stimulus ranges over which amplitude compression is noticed and, therefore, will increase the dynamic vary of the system. In Fig. 5A-C, we present {that a} nonisochronous oscillator compresses its response over a spread that’s greater than an order of magnitude bigger than within the isochronous case. To quantify this compression, we outline the dynamic vary of the system as

$$start{aligned} gamma = log _{10}{ bigg [frac{F_{10times sync}}{F_{sync}} bigg ]} finish{aligned}$$
(6)

the place (F_{sync}) is the minimal forcing amplitude required for the response of the system to synchronize to an exterior sinusoidal sign within the deterministic restrict. (F_{10times sync}) represents the forcing amplitude required to elicit an amplitude response 10 occasions as giant because the amplitude response at (F_{sync}). Subsequently, the dynamic vary, (gamma), measures what number of a long time of forcing energy span one decade of the response amplitude. We illustrate how this metric is calculated in Fig. 5D. For quiescent methods, we outline (F_{sync}) to be the forcing required to elicit an amplitude response of 0.01. For a linear system, we might discover (gamma = 1), whereas for a system that responds in response to the 1/3-power-law, we might anticipate (gamma = 3).

Within the deterministic restrict, the dynamic vary might be approximated analytically as

$$start{aligned} gamma = 3 + log _{10}{ Bigg [ frac{mu } bigg (1 + Big (frac{beta }{alpha }Big )^2 bigg ) Bigg ] } finish{aligned}$$
(7)

for (mu > 0) and (|Delta omega | > 0). The supplementary materials outlines this analytical calculation and discusses the dynamic vary within the quiescent regime. We evaluate this approximation to numerical simulations in Fig. 5E-F. This approximation, which predicts that nonisochronicity will increase the dynamic vary monotonically with rising (|beta |), reveals good settlement with numerical information within the deterministic restrict. Nonetheless, within the presence of noise, this approximation breaks down for big values of (|beta |) and for methods close to the Hopf bifurcation. Within the oscillatory regime, we as an alternative observe a peak within the dynamic vary as a perform of (|beta |), according to the beforehand mentioned values of (|beta |) that result in maximal sensitivity.

Determine 6
figure 6

(Narrowband detector) The heatmaps of the 5 metrics of significance are proven within the high row. These metrics are plotted as features of the extent of chaos for (mu approx 0.14) (A) and (mu approx 1.57) (B), as indicated by the yellow, dotted strains within the high panels. (C) Detection index from incorporating the 5 measures with equal weights, (textbf{w} = frac{1}{5}[1, 1, 0, 1, 0, 1, 1]). Cross-sections of the heatmap on the dotted strains are proven to the proper. For all measures proven, the noise energy was set to (D=10^{-3}), except for (tilde{Q}), the place the noise was thought to be an exterior stimulus. On this case, (D=0.01).

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Determine 7
figure 7

(Broadband detector) The heatmaps of the 4 metrics of significance are proven within the high row. These metrics are plotted as features of the extent of chaos for (mu approx 0.43) (A) and (mu approx 1.57) (B), as indicated by the yellow, dotted strains within the high panels. (C) Detection index from incorporating the 4 measures with equal weights, (textbf{w} = frac{1}{4}[0, 1, 1, 0, 1, 1, 0]). Cross-sections of the heatmap on the dotted strains are proven to the proper. For all measures proven, the noise energy was set to (D=10^{-3}), except for (tilde{BW}), the place the noise was thought to be an exterior stimulus. On this case, (D=0.01).

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Combining all response metrics

The only option of parameters relies upon closely on the appliance of the sign detector and the specified specs. For single-frequency or narrowband detectors, it might be favorable to have the standard issue of the response as excessive as attainable. Nonetheless, for a broadband detector, a big high quality issue could be unfavorable, as it will attenuate frequencies that ought to be captured. We word that there exists a spectrum of desired capabilities of sign detectors and no single metric can totally characterize the efficiency. We due to this fact incorporate all seven measures right into a single rating, which might be weighted in accordance with the appliance of the system. We first outline the vector

$$start{aligned} textbf{v} = start{bmatrix} tilde{chi }(omega _0),&tilde{TE}_{FM},&tilde{TE}_{AM},&tilde{Q},&tilde{BW},&tilde{frac{1}{tau }},&tilde{gamma } finish{bmatrix}, finish{aligned}$$
(8)

the place the weather of this vector are the on-resonance linear response, switch entropy from FM stimulus, switch entropy from AM stimulus, high quality issue, threshold bandwidth, pace of response, and dynamic vary, respectively. Every factor is linearly scaled such that its vary runs from 0 to 1 on the minimal and most values within the parameter house. To raised illustrate the optimum regime, we scale the measures such that they saturate on the ninetieth percentile of their distributions. Within the supplementary materials, we differ this saturation level to point out that it has a negligible impact on our outcomes (Figs. S3-S4). We then outline the detection index to be the sum of the weather in (textbf{v}), scaled by relative weights of significance,

$$start{aligned} textual content {detection index} = textbf{w} cdot textbf{v}, finish{aligned}$$
(9)

the place (textbf{w}) is a vector whose values symbolize the relative weights of significance of the measures.

The detection index may exhibit a number of optima, depending on the specified specs, or it may improve indefinitely with both (mu) or (beta), thus yielding no optimum. From the organic perspective, not solely do auditory and vestibular sensors have completely different calls for, however furthermore, the identical finish organs from completely different species are prone to be optimized for various environments and numerous conspecific calls. Moreover, even the identical system might self tune into completely different regimes when positioned in numerous acoustic environment. These variations can readily be captured by the suitable choice of the weighting issue, (textbf{w}). We present a attainable choice of weights that illustrates how a frequency-selective auditory organ might optimize its efficiency, and a choice of weighting components to explain the efficiency of a broadband detector. Within the supplementary materials, we current further weight decisions for each forms of detector (Figs. S3–S4).

A narrowband detector ought to prioritize the linear response perform at resonance, the standard issue of the response, the dynamic vary, the pace of response, and the switch entropy from a narrowband FM sign. Since a big threshold bandwidth could be dangerous to this detector’s objective, we set its weight to zero and use the burden vector, (textbf{w} = frac{1}{5}[1, 1, 0, 1, 0, 1, 1]). Utilizing these weights, we discover that the detection index seems to extend indefinitely with rising (mu), however peaks at small (|beta |) when (mu) is fastened (Fig. 6C).

We now contemplate a broadband detector, the place the edge bandwidth is essential as an alternative of the standard issue. The detector ought to be delicate to sign modulations and have the ability to detect transient indicators composed of many frequencies. On this case, the knowledge captured by sign modulations is effective, in addition to the pace of response. We due to this fact select the burden vector, (textbf{w} = frac{1}{4}[0, 1, 1, 0, 1, 1, 0]). The efficiency of this detector varies in a way just like the narrowband detector in that it continues to enhance deeply into the oscillatory regime. Nonetheless, a better diploma of nonisochronicity is preferable (Fig. 7C).

Dialogue

We now have decided the efficiency of the Hopf oscillator as a sign detector all through its parameter house, by various each the proximity to criticality and the diploma of nonisochronicity. To the most effective of our data, the intersection of those two properties has not beforehand been studied within the context of sign detection. We first calculated the Lyapunov exponent of the system within the (mu beta)-plane, close to the Hopf bifurcation, characterizing the extent of chaos induced by noise. We confirmed the breakdown of a earlier analytic approximation because the system approaches the Hopf bifurcation. As an alternative of diverging, the Lyapunov exponent decreases to zero repeatedly and turns into unfavorable for (mu < 0), as is predicted for dynamics close to a set level. This enables us to correlate the diploma of chaos with the nonisochronicity parameter, (beta).

Offering the oscillator with a number of forms of stimulus, we demonstrated that the sensitivity and temporal acuity of the nonisochronous system can exceed that of the essential system, whatever the stage of exterior noise. Additional, we confirmed that the nonisochronous system compresses the response of the system to large-amplitude indicators, as a result of improve in magnitude of the cubic parameter. This will increase the dynamic vary of stimulus amplitudes that the system can safely detect. All three of those measures (sensitivity, temporal acuity, and amplitude compression) have been proven to be essential points for sign detection by the auditory and vestibular methods.

The favorable parameter regime for frequency selectivity, when assessed in isolation, relies upon closely on the precise software of the detector. The tuning curves broaden because the system approaches criticality from the oscillatory aspect, and when the extent of nonisochronicity is elevated. Probably the most sharply-tuned detectors might be discovered when the system is isochronous and as far into the oscillatory regime as attainable. Therefore, the one one in every of our metrics that’s monotonically degraded by nonisochronicity is the standard issue. Nonetheless, the pace of response, (1/tau), monotonically will increase with rising ranges of nonisochronicity. The opposite 5 metrics are non-monotonically improved by nonisochronicity. For fastened (mu), every of those metrics can exhibit a peak as a perform of (beta) or the Lyapunov exponent (Figs. 6-7).

When contemplating all the measures collectively, the prefered parameter regime displays the organic software of the detector and the specified specs. The assorted necessities for auditory and vestibular detection have been beforehand explored with a numerical mannequin of a person hair cell bundle. In that research, the mechanical load of the system was proven to enormously affect the response metrics42. Within the current work, we purpose to grasp the results of nonisochronicity and criticality contemplating two situations. The primary is a single-frequency or narrowband detector. When incorporating all the related measures for this detector, we discover that the popular regime is within the oscillatory state, as removed from the Hopf bifurcation as attainable, and with a small quantity of nonisochronicity. On this regime, the innate dynamics are weakly chaotic. We suggest that this regime could be well-suited for frequency-selective auditory methods. For the second situation, we contemplate a system designed to detect as broad a spread of frequencies as attainable, and seize info from transient stimuli, which are likely to include many frequencies. On this case, we additionally discover that the popular regime is as deeply into the oscillatory regime as attainable, however with a big diploma of nonisochronicity. We suggest that this regime is well-suited for vestibular methods.

General, we present that every one detection metrics, aside from the standard issue, are enhanced by the presence of nonisochronicity. Particularly, the difficulty of essential slowing down is eliminated, whereas sensitivity is enhanced. Therefore, this tradeoff inherent in essential methods is resolved, and evaluating completely different regimes of detection – broadband versus frequency selective – modifications solely the popular stage of (beta). Lastly, excessive efficiency is achieved, not at a particular level, however relatively in a broad vary of parameter house, which might endow the organic system with flexibility and robustness, and obviate the necessity for very exact fine-tuning of parameters or the necessity for dynamical suggestions on any of them43.

We word that nonisochronicity and chaos are frequent components in high-dimensional dynamical methods that include nonlinearities, whatever the presence of stochastic processes. A earlier numerical research of a deterministic Hopf oscillator discovered that chaos seems upon introducing a suggestions equation on the management parameter44. We later demonstrated that this method reveals improved sensitivity on this chaotic regime20. Chaos was additionally present in a 12-dimensional numerical mannequin of hair cells41, and its presence was related to enhanced compliance to step-like stimuli. As an extra demonstration, we present that the Rössler attractor45 is most delicate to weak indicators when poised within the weakly chaotic regime (see Supplementary Materials, Figs. S5-S6). We anticipate chaotic dynamics to be current in different detailed fashions of auditory and vestibular methods, as they’re each excessive dimensional and nonlinear.

The present research considers solely single Hopf oscillators, representing particular person sensory components. Whereas the sensory hair bundles of sure species are free-standing46, hair bundles of most vestibular and auditory organs studied show some extent of mechanical coupling to one another. The energy and extent of this coupling varies enormously for various specializations of the sensory organ47. Contemplating the varied configurations of mechanical coupling, we now have centered this research solely on the power of particular person Hopf oscillators to detect exterior indicators. Understanding the joint results of chaos and criticality on a single oscillator is essential for understanding the total coupled system.

Nonetheless, we level out two problems with particular person detectors which were proven to resolve within the coupled system. The primary is the smearing out of the essential level within the presence of noise (Fig. S1). It has been proven that, though noise removes criticality within the particular person Hopf oscillator, it may be restored within the coupled system48. Second, though nonisochronicity deteriorates the frequency selectivity of particular person detectors, it may be restored in arrays of coupled detectors, offered that the weather synchronize to one another49.

Nonisochronicity is commonly excluded from easy numerical fashions of those sensory methods. It enormously will increase the complexity of the system, resulting in multi-stability and a chaotic response to varied forms of indicators19,38, together with white noise. Additional, the nonisochronous time period within the Hopf oscillator was proven to result in violation of a generalized model of the fluctuation dissipation theorem, breaking any easy relations between the system’s sensitivity to stimulus and susceptibility to stochastic fluctuations50. Within the isochronous image, poising a system close to the Hopf bifurcation might be useful. The switch entropy is maximized (Fig. 2), and the system can obtain giant, entrained responses to weak indicators, offered that the noise is sufficiently weak. Nonetheless, to make the system sturdy to noise, it should reside within the oscillatory regime, thereby using the power of the autonomous movement to amplify the sign. This leads to a tradeoff alongside the control-parameter axis, resulting in an optimum worth for (mu), the place the system is shut sufficient to the bifurcation to entrain to a sign, however oscillatory sufficient to be sturdy to noise39.

Nonetheless, when nonisochronicity is launched, this prefered parameter alternative strikes deeply into the oscillatory regime, whereas the entrainability can then be managed by (beta). This parameter might be adjusted to extend the detection capabilities of the system, with completely different most popular values that rely on the precise software of the system. We additionally word that this parameter controls the diploma of synchronization in an array of coupled Hopf oscillators51. We due to this fact suggest that, along with proximity to a essential level, nonisochronicity is a necessary factor within the dynamics of auditory and vestibular methods. This instability, which provides rise to chaotic dynamics, additionally enormously improves the efficiency and robustness of the Hopf oscillator as a sign detector. We speculate that nonisochronicity and chaos are essential traits of different organic methods developed for sign detection, in addition to methods that exhibit synchronization of their energetic elements.

Numerical strategies

Stochastic differential equations had been solved utilizing Heun’s technique with time steps starting from (2pi occasions 10^{-4}) to (2pi occasions 10^{-3}).

Response amplitude and linear response perform

To find out phase-locked amplitude, we first compute the typical response to a sinusoidal stimulus of 64 methods, every ready with completely different preliminary phases uniformly spaced throughout the deterministic restrict cycle. This technique ensures that any non-synchronized oscillations on the stimulus frequency will common to zero, and solely indicators that lock to the stimulus shall be counted towards the response. We then match a sinusoid to the imply response with frequency fastened to the stimulus frequency. We outline the phase-locked amplitude because the amplitude of this match. We then compute the linear response perform by dividing this response amplitude by the forcing amplitude.

Frequency-modulated (FM) and amplitude-modulated (AM) stimuli

The frequency-modulated forcing takes the shape

$$start{aligned} F_{FM}(t) = F_0 e^{ipsi (t)}, finish{aligned}$$
(10)

the place (psi (t)) is the instantaneous section of the stimulus, and we set (F_0 = 0.1). The instantaneous stimulus frequency is centered at (Omega _0), with additive stochastic fluctuations,

$$start{aligned} omega (t) = Omega _0 + eta _f(t), finish{aligned}$$
(11)

the place (eta _f(t)) is low-pass filtered Gaussian white noise (pink noise) with a brick-wall cutoff frequency of (Omega _0). We let the usual deviation of this variable equal (0.3times Omega _0). We will then calculate the instantaneous section of the stimulus,

$$start{aligned} psi (t) = int _0^t omega (t’) dt’ = psi (0) + Omega _0 t + int _0^t eta _f(t’) dt’. finish{aligned}$$
(12)

Info manufacturing is decided solely by the frequency modulator, (eta _f(t)). This technique of producing the sign doesn’t affect the amplitude and permits us to look at the results of data transmission by way of frequency modulation alone.

Equally, amplitude-modulated indicators take the shape

$$start{aligned} F_{AM}(t) = eta _a(t) e^{iOmega _0 t}, finish{aligned}$$
(13)

the place (eta _a(t)) is low-pass filtered Gaussian white noise (pink noise) with a brick-wall cutoff frequency of (Omega _0). We set the imply and normal deviation of this stochastic amplitude modulator to be 0 and 0.5, respectively.

Switch entropy

The switch entropy40 from course of J to course of I is outlined as

$$start{aligned} T_{J rightarrow I} = sum p(i_{n+1}, i_{n}^{(okay)}, j_{n}^{(l)}) log frac{p(i_{n+1} | i_{n}^{(okay)}, j_{n}^{(l)})}{p(i_{n+1} | i_{n}^{(okay)})}, finish{aligned}$$
(14)

the place (i_{n}^{(okay)} = (i_n,ldots ,i_{n-k+1})) are the okay most up-to-date states of course of I. Subsequently, (p(i_{n+1} | i_{n}^{(okay)}, j_{n}^{(l)})) is the conditional chance of discovering course of I in state (i_{n+1}) at time (n+1), on condition that the earlier okay states of course of I had been (i_{n}^{(okay)}) and that the earlier l states of course of J had been (j_{n}^{(l)}). The summation runs over all factors within the time sequence and over all accessible states of each processes. The switch entropy measures how a lot one’s means to foretell the way forward for course of I is improved upon studying the historical past of course of J. The measure is uneven upon switching I and J, as info switch between two processes will not be essentially symmetric. For stimulus-response information, this asymmetry permits for management assessments by measuring the switch entropy from the response to the stimulus, which ought to be zero. The selection of okay and l has little impact on the outcomes, so we choose (okay = l = 5), and pattern the 5 factors such that they span one imply interval of the system. We discretize the sign into 4 amplitude bins, nonetheless, related outcomes had been obtained when utilizing 2 bins.

Return time

We decide the return time, (tau), by calculating the typical response from 64 simulations to a large-amplitude step stimulus,

$$start{aligned} F(t) = F_{0}[Theta (t – t_{on}) – Theta (t – t_{off}) ], finish{aligned}$$
(15)

the place (Theta (t)) is the Heaviside step perform, and we set (F_{0} = 5), (t_{on} = 1), and (t_{off} = 2). We outline the return time because the time it takes the imply response of the system to return to a worth inside an ordinary deviation of the imply steady-state amplitude, as measured by the info previous to the step onset.

High quality issue and threshold bandwidth

To estimate the standard issue of the system response, we stimulate with additive white Gaussian noise ((D = 0.01)), and calculate the typical energy spectrum over 60 simulations, every with completely different preliminary situations and realizations of noise. This produces a clean curve, which might then be used to estimate the total width at half most and the standard issue of the response. We then use this curve to calculate threshold bandwidth by figuring out the vary of frequencies for which the Fourier amplitudes exceed a threshold of 0.1. This threshold was chosen as it’s roughly an order of magnitude above the noise flooring of the essential system.

Information availability

The Python code used for performing the evaluation and producing the figures is accessible on-line: https://github.com/jfaber3/Criticality-and-Chaos.git.

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Acknowledgements

The authors gratefully acknowledge the help of NSF Physics of Residing Techniques, beneath grant quantity 2210316.

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D.B. formulated the analysis questions. J.F. carried out the numerical simulations. Each authors wrote and reviewed the primary manuscript.

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Justin Faber.

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Faber, J., Bozovic, D. Criticality and chaos in auditory and vestibular sensing.
Sci Rep 14, 13073 (2024). https://doi.org/10.1038/s41598-024-63696-3

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  • Acquired: 19 January 2024

  • Accepted: 31 Could 2024

  • Revealed: 06 June 2024

  • DOI: https://doi.org/10.1038/s41598-024-63696-3

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