SO(2)-Networks as Neural Oscillators

  • Frank Pasemann
  • Manfred Hild
  • Keyan Zahedi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2686)


Using discrete-time dynamics of a two neuron networkw ith recurrent connectivity it is shown that for specific parameter configurations the output signals of neurons can be of almost sinusoidal shape. These networks live near the Sacker-Neimark bifurcation set, and are termed SO(2)-networks, because their weight matrices correspond to rotations in the plane. The discretized sinus-shaped waveform is due to the existence of quasi-periodic attractors. It is shown that the frequency of the oscillators can be controlled by only one parameter. Signals from the neurons have a phase shift of Π/2 and may be useful for various kinds of applications; for instance controlling the gait of legged robots.


Synaptic Weight Parameter Domain Neural Oscillator Quadruped Robot Periodic Attractor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Frank Pasemann
    • 1
  • Manfred Hild
    • 1
  • Keyan Zahedi
    • 1
  1. 1.Fraunhofer Institute for Autonomous Intelligent Systems (AiS)Sankt AugustinGermany

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