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Frequency modulation of large oscillatory neural networks

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Abstract

Dynamical systems which generate periodic signals are of interest as models of biological central pattern generators and in a number of robotic applications. A basic functionality that is required in both biological modelling and robotics is frequency modulation. This leads to the question of whether there are generic mechanisms to control the frequency of neural oscillators. Here we describe why this objective is of a different nature, and more difficult to achieve, than modulating other oscillation characteristics (like amplitude, offset, signal shape). We propose a generic way to solve this task which makes use of a simple linear controller. It rests on the insight that there is a bidirectional dependency between the frequency of an oscillation and geometric properties of the neural oscillator’s phase portrait. By controlling the geometry of the neural state orbits, it is possible to control the frequency on the condition that the state space can be shaped such that it can be pushed easily to any frequency.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their constructive comments that helped improve this manuscript. The research leading to the results presented here received funding from the European Community’s Seventh Framework Programme (EU FP7) under Grant Agreement 248311 Adaptive Modular Architecture for Rich Motor Skills (AMARSi).

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Authors and Affiliations

  1. Electronics and Information Systems Department, Ghent University, Sint-Pietersnieuwstraat 41, 9000 , Ghent, Belgium

    Francis wyffels, Tim Waegeman & Benjamin Schrauwen

  2. Jacobs University Bremen gGmbH, Campus Ring, 28759 , Bremen, Germany

    Jiwen Li & Herbert Jaeger

Authors
  1. Francis wyffels
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  2. Jiwen Li
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  3. Tim Waegeman
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  4. Benjamin Schrauwen
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  5. Herbert Jaeger
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Correspondence to Francis wyffels.

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wyffels, F., Li, J., Waegeman, T. et al. Frequency modulation of large oscillatory neural networks. Biol Cybern 108, 145–157 (2014). https://doi.org/10.1007/s00422-013-0584-0

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  • DOI: https://doi.org/10.1007/s00422-013-0584-0

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