Horn D, Opher I
School of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Israel.
Neural Comput. 1996 Feb 15;8(2):373-89. doi: 10.1162/neco.1996.8.2.373.
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.
振荡吸引子神经网络可以通过形成交错振荡来执行时间分割，即分离它们接收到的联合输入。这种特性可能是许多感知功能的基础，本文在对称动态系统的背景下对其进行了研究。完全分割模式是该系统可以发展出的一种极限环。它只能在有限数量n的振荡器中维持。对少量段的这种限制是此类系统中的一个基本现象。在我们的模型中，我们可以根据单个非线性振荡器窄次谐波解的有限范围来解释它。此外，这种观点使我们能够理解在部分分割解中三个主导振幅的优势，这是在高n时获得的。当我们用分级输入替换公共输入，允许不同的振荡器有不同的输入时，后者也很丰富。切换到具有波动分量的输入，我们对于小系统获得分割优势，而对于大系统获得相当不规则的波形。
