Sensory Systems/Vestibular System Simulation

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Vestibular Implants[edit]


People with damaged vestibular systems experience a combination of symptoms that may include hearing and vision disturbances, vertigo, dizziness, and spatial disorientation. Currently, there are no effective treatments for patients with weak or damaged vestibular systems. Over the past decade, scientists have developed an electrical stimulating device, similar to cochlear implants, that would restore semicircular canal function. Vestibular implants are intended to restore balance in patients with a damaged vestibular system. Figure[1] shows a vestibular implant prototype, which is a modified cochlear implant designed by MED-EL (Innsbruck, Austria).

Vestibular implant designed by MED-EL (Innsbruck, Austria).

This vestibular neuroprosthesis prototype contains four major components: an electrical stimulator, three extracochlear electrodes that are placed in the ampullae of each semicircular canal, and an intracochlear array. When the vestibular implant is turned on, trains of electrical stimulation in the form of charge-balance, biphasic pulses are delivered down each extracochlear electrode toward a respective vestibular nerve [1]. Ultimately, the electrical stimulation would restore balance in a patient by stabilizing gaze via the vestibulo-ocular reflex (VOR). Progress toward an implantable prosthesis has shown promising results to effectively restore normal vestibular sensory transduction of head rotations. However, achieving an accurate stimulation paradigm to chronically encode three-dimensional head movements without causing undesired neuronal activity remains one of several key challenges.

Vestibular prosthesis evolution (1963-2014)[edit]

In 1963, Cohen and Suzuki [2] introduced the notion of vestibular prosthesis by demonstrating that eye movements can be induced via electrical stimulation of the ampullary branch of a vestibular nerve. Studies that followed were driven to engineer a continuous and accurate stimulation model for rehabilitating patients with different types of vestibular disorders, such as bilateral loss of vestibular function (BVL) and Meniere's disease [1] [3]. Four decades after Cohen and Sukui's pioneering work, Merfeld and colleagues developed the first vestibular device for generating smooth eye movements by electrically stimulating the vestibular nerve [4] [5]. The feasibility of neuro-electronic vestibular devices had further inspired researchers to integrate a motion-detection system to measure head movements. Santina and colleagues [6] [7] [8] [9] used gyroscopic sensors to measure movements in three-dimensional space and encoded this information to generate signals that control muscles of each eye via the vestibular nerve. As of late 2012, only two groups in the world have conducted vestibular implant studies on humans: a team led by Jay Rubinstein at the University of Washington and a joint-effort between a team led by Herman Kingma at the Maastrict University of Medical Center in the Netherlands and second group led by Jean-Phillippe Guyot at Hopitaux Universitaries de Geneve, Switzerland [1]. Jay Rubinstein led the first vestibular clinical study in 2010. Rubinstein and colleagues had successfully installed a vestibular pacemaker to reduce or cease involuntary vertigo attacks in patients diagnosed with Meniere's disease [3]. This device was combined with a handheld controller to start and stop a range of electrical stimuli that can be directed to any or all electrodes, but did not code for motion [3]. Unfortunately, the vestibular pacemaker in implanted patients had resulted in both the auditory and vestibular function deteriorating considerably [10] [3] [1]. A new direction has been taken from this group to explore a different electrical stimulation paradigm by incorporating information about motion [10]. The second attempt for human clinical studies was carried by Kingma, Guyot, and colleagues in 2012. Vestibular implants used in this study were prototyped by MED-EL. Perez-Fornos and colleagues [1] demonstrated that patients achieved a level of satisfactory functional recovery that allows them to exercise everyday activities such as walking.

Current progress is being made through ongoing university-industry partnerships. There are four leading University and/or industry partnerships working toward a vestibular prosthesis for clinical applications. These teams include: Rubinstein at the University of Washington and Cochlear Ltd (Lane Cove, Australia), Della Santina's team at the Vestibular NeuroEngineering Laboratory [Johns Hopkins School of Medicine, Baltimore, MD, USA], Daniel Merfeld's team at the Jenks Vestibular Physiology Laboratory at Harvard [Massachusetts Eye and Ear Infirmary, Boston, MA, USA], and a joint-effort between Herman Kingma, Jean-Philippe Guyot, and MED-EL.

Future directions in research[edit]

The state-of-the-art vestibular implant technology is a two-step system that produces electrical stimulations to three ampullary nerves in response to rotations around a respective axis (anterior, posterior, or horizontal canals). However, the biophysics of prosthetic nerve stimulation remains a challenge to mimic normal sensory transduction. Even though much is already known about how vestibular nerve afferents encode head movements, it is not yet understood how to design a noninvasive stimulus encoding strategy for a multichannel prosthesis. Active research has continued to focus on overcoming design and signal transduction limitations.

Current neural prostheses are intended to excite neural tissues in which they are implanted, but the effect of continuous excitatory stimulations can yet cause neurological deficits [3]. Ultimately, a device that can both excite head motion in one direction and inhibit movement in the opposite direction is much desired. The latest prototype system developed by Santina and colleagues, SCSD1, has shown that direct current stimulations can evoke excitatory and inhibitory VOR responses [11]. Their results demonstrate that effects of introducing the vestibular system to an artificial baseline can possibly alter the dynamic ranges of excitatory and inhibitory thresholds in unpredicted ways. On the other hand, clinical studies show that it is possible for humans to adapt within a reasonably short time (a few minutes) to the absence and presence of artificial neural activity [12]. Once adaptation is reached, then one can tune the amplitude and frequency modulations of the stimulation to elicit smooth eye movements of different speeds and directions [12].

Another type of design limitation of electrical prosthesis is current to spread away from the targeted nerve tissue and cause stimulations in the wrong canal [13] [14]. As a consequence, this current spread induces misalignment between the axis of the eye and head rotation [15]. Therefore, the mechanisms underlying directional neural plasticity can provide well-aligned responses for humans. Other studies suggest infrared nerve stimulation is advantageous for targeting specific neurons and less obtrusive to nearby populations of neurons [13] [15]. The use of optics would allow higher spatial selectivity and improved surgical access [13].

In addition, a fundamental challenge underlying the development of vestibular prosthesis is accounting for ways in which information from vestibular end organs can elicit particular movements. It has been shown that reflex and perceptual responses are dependent on which vestibular afferent inputs are stimulated [10]. Surgical practices are examined for accurate placements of the electrode with respect to the afferents, which in the end could greatly influence the ability to stimulate a desired response.

Because the auditory and vestibular areas of the inner ear are connected, the spread of current beyond the target ampullary nerves and/or risks of surgery could interfere with cochlear nerve activity. It is likely that humans with implants will experience a risk of hearing loss, as observed in rhesus monkeys [16]. Santina and colleagues [16] found that implantation of electrodes caused up to 14 dB of hearing loss and delivery of electrical stimulation further reduced hearing by 0.4-7.8 dB. This study suggests that current spread to cochlear hair cells may cause random activity in nearby cochlear regions.

Computer Simulation of the Vestibular System[edit]

Semicircular Canals[edit]

Model without Cupula[edit]

Simplified semicircular canal, without cupula.

Let us consider the mechanical description of the semi-circular canals (SCC). We will make very strong and reductive assumptions in the following description. The goal here is merely to understand the very basic mechanical principles underlying the semicircular canals.

The first strong simplification we make is that a semicircular canal can be modeled as a circular tube of “outer” radius R and “inner” radius r. (For proper hydro mechanical derivations see (Damiano and Rabbitt 1996) and Obrist (2005)). This tube is filled with endolymph.

The orientation of the semicircular canal can be described, in a given coordinate system, by a vector  \vec n that is perpendicular to the plane of the canal. We will also use the following notations:

 \theta Rotation angle of tube [rad]
 \dot{\theta} \equiv \frac{d \theta}{dt} Angular velocity of the tube [rad/s]
 \ddot{\theta} \equiv \frac{d^2 \theta}{dt^2} Angular acceleration of the tube [rad/s^2]
 \phi Rotation angle of the endolymph inside the tube [rad], and similar notation for the time derivatives
 \delta = \theta - \phi movement between the tube and the endolymph [rad].

Note that all these variables are scalar quantities. We use the fact that the angular velocity of the tube can be viewed as the projection of the actual angular velocity vector of the head  \vec \omega onto the plane of the semicircular canal described by  \vec n to go from the 3D environment of the head to our scalar description. That is,

 \dot{\theta} = \vec \omega \cdot \vec n

where the standard scalar product is meant with the dot.

To characterize the endolymph movement, consider a free floating piston, with the same density as the endolymph. Two forces are acting on the system:

  1. The inertial moment  I \ddot{\phi} , where I characterizes the inertia of the endolymph.
  2. The viscous moment  B \dot{\delta} , caused by the friction of the endolymph on the walls of the tube.

This gives the equation of motion

 I \ddot{\phi} = B \dot{\delta}

Substituting  \phi = \theta - \delta and integrating gives

 \dot{\theta} = \dot{\delta} + \frac{B}{I} \delta .

Let us now consider the example of a velocity step  \dot{\theta}(t) of constant amplitude  \omega . In this case, we obtain a displacement

 \delta = \frac{I}{B} \omega \cdot (1-e^{-\frac{B}{I}t})

and for  t \gg \frac{I}{B} , we obtain the constant displacement

 \delta \approx \frac{I}{B} \omega .

Now, let us derive the time constant  T_1 \equiv \frac{I}{B} . Fora thin tube,  r \ll R , the inertia is approximately given by

 I = m l^2 \approx 2 \rho \pi^2 r^2 R^3 .

From the Poiseuille-Hagen Equation, the force F from a laminar flow with velocity v in a thin tube is

 F = \frac{8 \bar{V} \eta l}{r^2}

where  \bar{V} = r^2 \pi v is the volume flow per second,  \eta the viscosity and  l = 2 \pi R the length of the tube.

With the torque  M = F \cdot R and the relative angular velocity  \Omega = \frac{v}{R} , substitution provides

 B = \frac{M}{\Omega} = 16 \eta \pi ^2 R^3

Finally, this gives the time constant  T_1

 T_1 = \frac{I}{B} = \frac{\delta r^2}{8 \eta}

For the human balance system, replacing the variables with experimentally obtained parameters yields a time constant  T_1 of about 0.01 s. This is brief enough that in equation (10.5) the  \approx can be replaced by " = ". This gives a system gain of

 G \equiv \frac{\delta}{\omega} = \frac{I}{B} = T_1

Model with Cupula[edit]

Effect of the cupula.

Our discussion until this point has not included the role of the cupula in the SCC: The cupula acts as an elastic membrane that gets displaced by angular accelerations. Through its elasticity the cupula returns the system to its resting position. The elasticity of the cupula adds an additional elastic term to the equation of movement. If it is taken into account, this equation becomes

 \ddot{\theta} = \ddot{\delta} + \frac{B}{I} \dot{\delta} + \frac{K}{I} \delta

An elegant way to solve such differential equations is the Laplace-Transformation. The Laplace transform turns differential equations into algebraic equations: if the Laplace transform of a signal x(t) is denoted by X(s), the Laplace transform of the time derivative is

 \frac{dx(t)}{dt} \xrightarrow{Laplace Transform} s \cdot X(s) - x(0)

The term x(0) details the starting condition, and can often be set to zero by an appropriate choice of the reference position. Thus, the Laplace transform is

 s^2 \tilde{\theta} = s^2 \tilde{\delta} + \frac{B}{I} s \tilde{\delta} + \frac{K}{I} \tilde{\delta}

where "~" indicates the Laplace transformed variable. With  T_1 from above, and  T_2 defined by

 T_2 = \frac{B}{K}

we get the

 \frac{ \tilde{\delta} }{ \tilde{\theta} } = \frac{T_1 s^2}{T_1 s^2 + s + \frac{1}{T_2}}

For humans, typical values for  T_2 = B/K are about 5 sec.

To find the poles of this transfer function, we have to determine for which values of s the denominator equals 0:

 s_{1,2} = \frac{1}{T_1} \Big(-1 \pm \sqrt{1-4\frac{T_1}{T_2}} \Big)

Since  T_2 \gg T_1 , and since

 \sqrt{1-x} \approx 1 - \frac{x}{2} for x \ll 1

we obtain

 s_1 \approx - \frac{1}{T_1}, and s_2 \approx - \frac{1}{T_2}

Typically we are interested in the cupula displacement  \delta as a function of head velocity  \dot{\theta} \equiv s \tilde{\theta} :

 \frac{\tilde{\delta}}{s \tilde{\theta}}(s) = \frac{T_1 T_2 s}{(T_1 s +1)(T_2 s + 1)}

For typical head movements (0.2 Hz < f < 20Hz), the system gain is approximately constant. In other words, for typical head movements the cupula displacement is proportional to the angular head velocity!

Bode plot of the cupula displacement of a function of head velocity, with T1 = 0.01 sec, T2 = 5 sec, and an amplification factor of (T1+ T2)/ (T1* T2) to obtain a gain of approximately 0 for the central frequencies.

Control Systems[edit]

For Linear, Time-Invariant systems (LTI systems), the input and output have a simple relationship in the frequency domain :

 Out(s) = G(s)*In(s)

where the transfer function G(s) can be expressed by the algebraic function


In other words, specifying the coefficients of the numerator (n) and denominator (d) uniquely characterizes the transfer function. This notation is used by some computational tools to simulate the response of such a system to a given input.

Different tools can be used to simulate such a system. For example, the response of a low-pass filter with a time-constant of 7 sec to an input step at 1 sec has the following transfer function


and can be simulated as follows:

With Simulink[edit]
Step-response simulation of a lowpass filter with Simulink.

If you work on the command line, you can use the Control System Toolbox of MATLAB or the module signal of the Python package SciPy:

MATLAB Control System Toolbox:

% Define the transfer function
num = [1];
tau = 7;
den = [tau, 1];
mySystem = tf(num,den)
% Generate an input step
t = 0:0.1:30;
inSignal = zeros(size(t));
inSignal(t>=1) = 1;
% Simulate and show the output
[outSignal, tSim] = lsim(mySystem, inSignal, t);
plot(t, inSignal, tSim, outSignal);

Python - SciPy:

# Import required packages
import numpy as np
import scipy.signal as ss
import matplotlib.pylab as mp
# Define transfer function
num = [1]
tau = 7
den = [tau, 1]
mySystem = ss.lti(num, den)
# Generate inSignal
t = np.arange(0,30,0.1)
inSignal = np.zeros(t.size)
inSignal[t>=1] = 1
# Simulate and plot outSignal
tout, outSignal, xout = ss.lsim(mySystem, inSignal, t)
mp.plot(t, inSignal, tout, outSignal)


Consider now the mechanics of the otolith organs. Since they are made up by complex, visco-elastic materials with a curved shape, their mechanics cannot be described with analytical tools. However, their movement can be simulated numerically with the finite element technique. Thereby the volume under consideration is divided into many small volume elements, and for each element the physical equations are approximated by analytical functions.

FE-Simulations: Small, finite elements are used to construct a mechanical model; here for example the saccule.

Here we will only show the physical equations for the visco-elastic otolith materials. The movement of each elastic material has to obey Cauchy’s equations of motion:

 \rho \frac{\partial^2 u_i}{\partial t^2} = \rho B_i + \sum_{j} \frac{\partial T_{ij}}{\partial x_j}

where  \rho is the effective density of the material,  u_i the displacements along the i-axis,  B_i the i-component of the volume force, and  T_{ij} the components of the Cauchy’s strain tensor.  x_j are the coordinates.

For linear elastic, isotropic material, Cauchy’s strain tensor is given by

 T_{ij} = \lambda e \delta_{ij} + 2 \mu E_{ij}

where  \lambda and  \mu are the Lamé constants;  \mu is identical with the shear modulus.  e = div(\vec u) , and  E_{ij} is the stress tensor

 E_{ij} = \frac{1}{2} \Big( \frac{\partial u_i}{\partial x_j} + \frac{\partial u_j}{\partial x_i} \Big).

This leads to Navier’s Equations of motion

 \rho \frac{\partial ^2 u_i}{\partial t^2} = \rho B_i + (\lambda + \mu) \frac{\partial e}{\partial x_i} + \mu \sum_{j} \frac{\partial ^2 u_i}{\partial x_j^2}

This equation holds for purely elastic, isotropic materials, and can be solved with the finite element technique. A typical procedure to find the mechanical parameters that appear in this equation is the following: when a cylindrical sample of the material is put under strain, the Young coefficient E characterizes the change in length, and the Poisson’s ratio  \nu the simultaneous decrease in diameter. The Lamé constants  \lambda and  \mu are related to E and  \nu by:

 E = \frac{\mu (3 \lambda + 2 \mu)}{\lambda + \mu}


 \nu = \frac{\lambda}{2(\lambda + \mu)}

Central Vestibular Processing[edit]

Central processing of vestibular information significantly affects the perceived orientation and movement in space. The corresponding information processing in the brainstem can often be modeled efficiently with control-system tools. As a specific example, we show how to model the effect of velocity storage.

Velocity Storage[edit]

The concept of velocity storage is based on the following experimental finding: when we abruptly stop from a sustained rotation about an earth-vertical axis, the cupula is deflected by the deceleration, but returns to its resting state with a time-constant of about 5 sec. However, the perceived rotation continues much longer, and decreases with a much longer time constant, typically somewhere between 15 and 20 sec.

Vestibular Modeling: The blue curve describes the deflection of the cupula as a response to a velocity step, modeled as a high-pass filter with a time-constant of 5 sec. The green curve represents the internal estimate of the angular velocity, obtained with an internal model of the cupula-response in a negative feedback look, and a feed-forward gain-factor of 2.

In the attached figure, the response of the canals to an angular velocity stimulus ω is modeled by the transfer function C, here a simple high-pass filter with a time constant of 5 sec. (The canal response is determined by the deflection of the cupula, and is approximately proportional to the neural firing rate.) To model the increase in time constant, we assume that the central vestibular system has an internal model of the transfer function of the canals, \hat{C}. Based on this internal model, the expected firing rate of the internal estimate of the angular velocity, \hat{\omega}, is compared to the actual firing rate. With a the gain-factor k set to 2, the output of the model nicely reproduces the increase in the time constant. The corresponding Python code can be found at [17].

It is worth noting that this feedback loop can be justified physiologically: we know that there are strong connections between the left and right vestibular nuclei. If those connections are severed, the time constant of the perceived rotation decreases to the peripheral time-constant of the semicircular canals.

Central Vestibular Processing can often be described with control-system models. Here "omega" is the head velocity, "C" the transfer function of the semicircular canals, and "k" a simple gain factor. The "hat"-ed variables indicate internal estimates.

Mathematically, negative feedback with a high gain has the interesting property that it can practically invert the transfer function in the negative feedback loop: if k>>1, and if the internal model of the canal transfer function is similar to the actual transfer function, the estimated angular velocity corresponds to the actual angular velocity.

  & \hat{\omega }=(\omega C-\hat{\omega }\hat{C})k \\ 
 & \hat{\omega }(1+\hat{C}k)=\omega Ck \\ 
 & \frac{{\hat{\omega }}}{\omega }=\frac{C}{1/k+\hat{C}}\,\,\xrightarrow[if\,C\approx \hat{C}]{k>>1}1  

Alcohol and the Vestibular System[edit]

As you may or may not know from personal experience, consumption of alcohol can also induce a feeling of rotation. The explanation is quite straightforward, and basically relies on two factors: i) alcohol is lighter than the endolymph; and ii) once it is in the blood, alcohol gets relatively quickly into the cupula, as the cupula has a good blood supply. In contrast, it diffuses only slowly into the endolymph, over a period of a few hours. In combination, this leads to a buoyancy of the cupola soon after you have consumed (too much) alcohol. When you lie on your side, the deflection of the left and right horizontal cupulae add up, and induce a strong feeling of rotation. The proof: just roll on the other side - and the perceived direction of rotation will flip around!

Due to the position of the cupulae, you will experience the strongest effect when you lie on your side. When you lie on your back, the deflection of the left and right cupula compensate each other, and you don't feel any horizontal rotation. This explains why hanging one leg out of the bed slows down the perceived rotation.

The overall effect is minimized in the upright head position - so try to stay up(right) as long as possible during the party!

If you have drunk way too much, the endolymph will contain a significant amount of alcohol the next morning - more so than the cupula. This explains while at that point, a small amount of alcohol (e.g. a small beer) balances the difference, and reduces the feeling of spinning.


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  2. Cohen, B. and Suzuki, J. (1963). "Eye movements induced by ampullary nerve stimulation.". The American journal of physiology 204: 347-351. 
  3. a b c d e Golub, J. S.; Ling, L.; Nie, K.; Nowack, A.; Shepherd, S. J.; Bierer, S. M.; Jameyson, E.; Kaneko, C. R.; Phillips, J. O.; and Rubinstein, J. T. (2014). "Prosthetic Implantation of the Human Vestibular System.". Otology & Neurotology 1: 136–147. 
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  6. Dai, C.; Fridman, G. Y.; Chiang, B.; Davidovics, N.; Melvin, T.; Cullen, K. E. and Della Santina, Charles C. (2011). "Cross-axis adaptation improves 3D vestibulo-ocular reflex alignment during chronic stimulation via a head-mounted multichannel vestibular prosthesis.". Experimental Brain Research 210: 595-606. 
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  8. Dai, Chenkai and Fridman, Gene Y. and Chiang, Bryce and Rahman, Mehdi A. and Ahn, Joong Ho and Davidovics, Natan S. and Della Santina, Charles C. (2013). "Directional Plasticity Rapidly Improves 3D Vestibulo-Ocular Reflex Alignment in Monkeys Using a Multichannel Vestibular Prosthesis.". Journal of the Association for Research in Otolaryngology 14: 863-877. 
  9. Davidovics, Natan S. and Rahman, Mehdi A. and Dai, Chenkai and Ahn, JoongHo and Fridman, Gene Y. and Della Santina, Charles C. (2013). "Multichannel Vestibular Prosthesis Employing Modulation of Pulse Rate and Current with Alignment Precompensation Elicits Improved VOR Performance in Monkeys.". Journal of the Association for Research in Otolaryngology 14: 233-248. 
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  11. Fridman, Gene Y. and Della Santina, Charles C. (2013). "Safe Direct Current Stimulation to Expand Capabilities of Neural Prostheses.". IEEE Trans Neural Syst Rehabil Eng. 21: 319-328. 
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  15. a b Lumbreras, Vicente and Bas, Esperanza and Gupta, Chhavi and Rajguru, Suhrud M. (2014). "Pulsed Infrared Radiation Excites Cultured Neonatal Spiral and Vestibular Ganglion Neurons by Modulating Mitochondrial Calcium Cycling.". Journal of Neurophysiology. 
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  17. Thomas Haslwanter (2013). "Vestibular Processing: Simulation of the Velocity Storage [Python"]. 

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