TEMPORAL SEGMENTATION IN A NEURAL DYNAMIC SYSTEM

Oscillatory attractor neural networks can perform temporal segmentatio n, i.e., separate the joint inputs they receive, through the formation of staggered oscillations. This property, which may be basic to many perceptual functions, is investigated here in the context of a symmetr ic dynamic system. The fully segmented mode is one type of limit cycle that this system can develop. It can be sustained for only a limited number n of oscillators. This limitation to a small number of segments is a basic phenomenon in such systems. Within our model we can explai n it in terms of the limited range of narrow subharmonic solutions of the single nonlinear oscillator. Moreover, this point of view allows u s to understand the dominance of three leading amplitudes in solutions of partial segmentation, which are obtained for high I?. The latter a re also abundant when we replace the common input with a graded one, a llowing for different inputs to different oscillators. Switching to an input with fluctuating components, we obtain segmentation dominance f or small systems and quite irregular waveforms for large systems.