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Biological Cybernetics

, Volume 75, Issue 3, pp 239–251 | Cite as

A neural network model for stroboscopic alternative motion

  • Hans-Otto Carmesin
  • Stefan Arndt

Abstract.

A neural network which models multistable perception is presented. The network consists of sensor and inner neurons. The dynamics is established by a stochastic neuronal dynamics, a formal Hebb-type coupling dynamics and a resource mechanism that corresponds to saturation effects in perception. From this a system of coupled differential equations is derived and analyzed. Single stimuli are bound to exactly one percept, even in ambiguous situations where multistability occurs. The network exhibits discontinuous as well as continuous phase transitions and models various empirical findings, including the percepts of succession, alternative motion and simultaneity; the percept of oscillation is explained by oscillating percepts at a continuous phase transition.

Keywords

Differential Equation Neural Network Phase Transition Network Model Neural Network Model 
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 1996

Authors and Affiliations

  • Hans-Otto Carmesin
    • 1
  • Stefan Arndt
    • 1
  1. 1.Department of Theoretical Neurophysics and Center for Cognitive Sciences, University of Bremen, D-28334 Bremen, GermanyDE

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