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This article is part of the supplement: Nineteenth Annual Computational Neuroscience Meeting: CNS*2010

Open Access Poster Presentation

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

Florian Jug*, Christoph Krautz and Angelika Steger

Author Affiliations

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

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BMC Neuroscience 2010, 11(Suppl 1):P111  doi:10.1186/1471-2202-11-S1-P111


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/11/S1/P111


Published:20 July 2010

© 2010 Jug et al; licensee BioMed Central Ltd.

Poster Presentation

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).

thumbnailFigure 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.

Acknowledgements

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

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. PubMed Abstract | Publisher Full Text OpenURL

  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 Abstract | Publisher Full Text | PubMed Central Full Text OpenURL