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• W2022382775 abstract "Oscillatory attractor neural networks can perform temporal segmentation, 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 symmetric 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 explain it in terms of the limited range of narrow subharmonic solutions of the single nonlinear oscillator. Moreover, this point of view allows us to understand the dominance of three leading amplitudes in solutions of partial segmentation, which are obtained for high n. The latter are also abundant when we replace the common input with a graded one, allowing for different inputs to different oscillators. Switching to an input with fluctuating components, we obtain segmentation dominance for small systems and quite irregular waveforms for large systems." @default.
• W2022382775 created "2016-06-24" @default.
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• W2022382775 date "1996-02-15" @default.
• W2022382775 modified "2023-10-17" @default.
• W2022382775 title "Temporal Segmentation in a Neural Dynamic System" @default.
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• W2022382775 doi "https://doi.org/10.1162/neco.1996.8.2.373" @default.
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