SO(2)-Networks as Neural Oscillators | Zendy

Frank Pasemann | Zendy; Manfred Hild | Zendy; Keyan Ghazi-Zahedi | Zendy

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SO(2)-Networks as Neural Oscillators

Author(s) -

Frank Pasemann,

Manfred Hild,

Keyan Ghazi-Zahedi

Publication year - 2003

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-40210-1

DOI - 10.1007/3-540-44868-3_19

Subject(s) - waveform , computer science , attractor , bifurcation , artificial neural network , discretization , topology (electrical circuits) , control theory (sociology) , set (abstract data type) , plane (geometry) , mathematics , mathematical analysis , artificial intelligence , physics , geometry , control (management) , combinatorics , telecommunications , radar , nonlinear system , quantum mechanics , programming language

Using discrete-time dynamics of a two neuron network with 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 pi/2 and may be useful for various kinds of applications; for instance controlling the gait of legged robots

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