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Frequency modulation of large oscillatory neural networks

(2014) BIOLOGICAL CYBERNETICS. 108(2). p.145-157
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AMARSI (Adaptive Modular Architecture for Rich Motor Skills)
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.
Keywords
ADAPTATION, Frequency modulation, BEHAVIORS, CIRCUITS, MODELS, ROBOT, Reservoir computing, Pattern generators, CENTRAL PATTERN GENERATORS, WALKING, LOCOMOTION

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Citation

Please use this url to cite or link to this publication:

Chicago
wyffels, Francis, Jiwen Li, Tim Waegeman, Benjamin Schrauwen, and Herbert Jaeger. 2014. “Frequency Modulation of Large Oscillatory Neural Networks.” Biological Cybernetics 108 (2): 145–157.
APA
wyffels, F., Li, J., Waegeman, T., Schrauwen, B., & Jaeger, H. (2014). Frequency modulation of large oscillatory neural networks. BIOLOGICAL CYBERNETICS, 108(2), 145–157.
Vancouver
1.
wyffels F, Li J, Waegeman T, Schrauwen B, Jaeger H. Frequency modulation of large oscillatory neural networks. BIOLOGICAL CYBERNETICS. 2014;108(2):145–57.
MLA
wyffels, Francis, Jiwen Li, Tim Waegeman, et al. “Frequency Modulation of Large Oscillatory Neural Networks.” BIOLOGICAL CYBERNETICS 108.2 (2014): 145–157. Print.
@article{4292780,
  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.},
  author       = {wyffels, Francis and Li, Jiwen and Waegeman, Tim and Schrauwen, Benjamin and Jaeger, Herbert},
  issn         = {0340-1200},
  journal      = {BIOLOGICAL CYBERNETICS},
  keyword      = {ADAPTATION,Frequency modulation,BEHAVIORS,CIRCUITS,MODELS,ROBOT,Reservoir computing,Pattern generators,CENTRAL PATTERN GENERATORS,WALKING,LOCOMOTION},
  language     = {eng},
  number       = {2},
  pages        = {145--157},
  title        = {Frequency modulation of large oscillatory neural networks},
  url          = {http://dx.doi.org/10.1007/s00422-013-0584-0},
  volume       = {108},
  year         = {2014},
}

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