Orosz Gábor, Ashwin Peter, Townley Stuart
Department of Mechanical Engineering, University of California at Santa Barbara, Santa Barbara, CA 93106, USA.
IEEE Trans Neural Netw. 2009 Jul;20(7):1135-47. doi: 10.1109/TNN.2009.2016658. Epub 2009 May 27.
In this paper, we consider a learning strategy that allows one to transmit information between two coupled phase oscillator systems (called teaching and learning systems) via frequency adaptation. The dynamics of these systems can be modeled with reference to a number of partially synchronized cluster states and transitions between them. Forcing the teaching system by steady but spatially nonhomogeneous inputs produces cyclic sequences of transitions between the cluster states, that is, information about inputs is encoded via a "winnerless competition" process into spatio-temporal codes. The large variety of codes can be learned by the learning system that adapts its frequencies to those of the teaching system. We visualize the dynamics using "weighted order parameters (WOPs)" that are analogous to "local field potentials" in neural systems. Since spatio-temporal coding is a mechanism that appears in olfactory systems, the developed learning rules may help to extract information from these neural ensembles.
在本文中，我们考虑一种学习策略，该策略允许通过频率自适应在两个耦合的相位振荡器系统（称为教学系统和学习系统）之间传输信息。这些系统的动力学可以参考一些部分同步的簇状态及其之间的转变来建模。通过稳定但空间上非均匀的输入对教学系统进行强迫，会产生簇状态之间的循环转变序列，也就是说，关于输入的信息通过“无胜者竞争”过程被编码到时空编码中。学习系统可以通过将其频率调整为与教学系统的频率相适应来学习各种各样的编码。我们使用类似于神经系统中“局部场电位”的“加权序参量（WOP）”来可视化动力学。由于时空编码是嗅觉系统中出现的一种机制，所开发的学习规则可能有助于从这些神经集合中提取信息。
