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BMC Neuroscience

Open Access

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

BMC Neuroscience201011(Suppl 1):P111

https://doi.org/10.1186/1471-2202-11-S1-P111

Published: 20 July 2010

Keywords

Upright PositionVestibular ApparatusEfferent SignalHebbian LearningVestibular 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.

Methods

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

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.

Results

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.

Declarations

Acknowledgements

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

Authors’ Affiliations

(1)
Institute of Theoretical Computer Science, ETH Zurich, Zurich, Switzerland

References

  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

Copyright

© Jug et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd.

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