arXiv 0908.42202009-08-28nlin.AO

耦合相位振子网络中学习率相关的聚类和自发展

Learning-rate dependent clustering and self-development in a network of coupled phase oscillators

Ritwik K. Niyogi, L. Q. English

发表信息：: Phys. Rev. E 80, 066213 (2009).
DOI：: 10.1103/PhysRevE.80.066213

摘要：

我们研究的作用，在仓本模型的耦合相位振荡器的耦合系数动态变化根据赫布学习规则的学习率。根据赫布理论，如果两个神经元同时共同活动，它们之间的突触就会增强。当学习率大于某个临界值时，出现两个稳定的反相同步簇。在这种快速学习的情况下，网络最终会将自己构建成一个全对全的耦合结构，而不管连接的初始条件如何。相比之下，当学习速度慢于这个临界值时，只有一个同步的集群可以发展。扩展我们的分析，我们探讨是否可以通过自发神经同步和赫布学习之间的相互作用，实现神经元网络的自我发展。我们发现，如果学习速度太慢，这种神经系统的自我发展是不可能的。最后，我们证明了类似于长期记忆的获得和巩固，这个网络能够产生和记住稳定的模式。

英文摘要：

We investigate the role of the learning rate in a Kuramoto Model of coupled phase oscillators in which the coupling coefficients dynamically vary according to a Hebbian learning rule. According to the Hebbian theory, a synapse between two neurons is strengthened if they are simultaneously co-active. Two stable synchronized clusters in anti-phase emerge when the learning rate is larger than a critical value. In such a fast learning scenario, the network eventually constructs itself into an all-to-all coupled structure, regardless of initial conditions in connectivity. In contrast, when learning is slower than this critical value, only a single synchronized cluster can develop. Extending our analysis, we explore whether self-development of neuronal networks can be achieved through an interaction between spontaneous neural synchronization and Hebbian learning. We find that self-development of such neural systems is impossible if learning is too slow. Finally, we demonstrate that similar to the acquisition and consolidation of long-term memory, this network is capable of generating and remembering stable patterns.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

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