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  • Poster Presentation
  • Open Access

A model of functional recovery after significant loss of neural tissue: biofeedback based healing of vestibular dysfunction

BMC Neuroscience201011 (Suppl 1) :P111

  • Published:


  • Upright Position
  • Vestibular Apparatus
  • Efferent Signal
  • Hebbian Learning
  • Vestibular Dysfunction

Vestibular dysfunction can significantly affect balance, posture, and gait. Hundreds of patients suffering from significant loss of neural (vestibular) tissue were helped with a new treatment using biofeedback – a strip of electrodes feeding head-tilt information onto the tongue surface [1, 2]. The success rate is stunning but the neural processes associated with this treatment are, to date, not understood in detail.

We present a model that can explain how a minor fraction of remaining vestibular tissue, trained using biofeedback, regains the ability to balance the modeled organism in an upright position.


Our model contains 4 populations of rate-coded units with sigmoid activation functions that are either not or fully connected via activity modulated Hebbian synapses (see Figure 1). A vestibular apparatus (VA) senses the tilt level of the modeled organism. VA is connected to a hidden population (HL) connected to a motor control population (BA), generating balancing actions and thereby closing a control loop by influencing the current tilt level. A second loop, the biofeedback, contains a population mimicking the signal of the mentioned tongue strip (TS).
Figure 1
Figure 1

Model architecture. Ellipses: populations; blue (darker) arrows: directed, full connectivity; gray arrows: causal dependencies, i.e., sensing or acting. Abbreviations are explained in the text. Quadratic insets show the initial weight matrices, white coding for high values.

VA and TS create population-coded output because their units are broadly tuned to different preferred tilt levels. HL and BA use winner take all dynamics. All units receive, in addition to the afferent input, a constant amount of white noise. Feedback connections from BA to HL force these populations to commit to a common, converged state.

Destroyed VA-units reduce the total input to HL. Homeostatic input normalization iteratively strengthens remaining postsynaptic processes to regain the desired input strength.


After destruction of a significant amount of VA-nodes (>90%) the remaining efferent signal does not exceed HL’s noise level and the entire system turns non-functional. During homeostatic input normalization the tuning of remaining efferent VA connections broadens and causes the system to settle in a non-functional state.

Biofeedback substitutes missing vestibular data and re-enables BA to generate sensible actions. BA-HL-feedback forces HL’s output to be correlated with the sensed tilt angles. Thus, activity modulated Hebbian learning re-sharpens VA’s efferent tuning and the modeled organism relearns to balance in an upright position – even without biofeedback. Phenomenologically this effect is also observed in human patients.



The authors would like to thank ETH Research Grant ETH-23 08-1.

Authors’ Affiliations

Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland


  1. Tyler M, Danilov YP, Bach-Y-Rita P: Closing an open-loop control system: vestibular substitution through the tongue. J Integr Neurosci. 2003, 2 (2): 159-164. 10.1142/S0219635203000263.View ArticlePubMedGoogle Scholar
  2. Danilov YP, Tyler ME, Skinner KL, Hogle RA, Bach-y-Rita P: Efficacy of electrotactile vestibular substitution in patients with peripheral and central vestibular loss. J Vestib Res. 2007, 17 (23): 119-130.PubMed CentralPubMedGoogle Scholar


© Jug et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd.