Bernstein Center for Computational Neuroscience, Berlin, Germany.
Philos Trans A Math Phys Eng Sci. 2011 Oct 13;369(1952):3730-47. doi: 10.1098/rsta.2011.0121.
Recent studies of brain connectivity and language with methods of complex networks have revealed common features of organization. These observations open a window to better understand the intrinsic relationship between the brain and the mind by studying how information is either physically stored or mentally represented. In this paper, we review some of the results in both brain and linguistic networks, and we illustrate how modelling approaches can serve to comprehend the relationship between the structure of the brain and its function. On the one hand, we show that brain and neural networks display dynamical behaviour with optimal complexity in terms of a balance between their capacity to simultaneously segregate and integrate information. On the other hand, we show how principles of neural organization can be implemented into models of memory storage and recognition to reproduce spontaneous transitions between memories, resembling phenomena of memory association studied in psycholinguistic experiments.
最近，利用复杂网络方法对大脑连接和语言进行的研究揭示了组织的共同特征。这些观察结果为通过研究信息是如何物理存储或心理表示来更好地理解大脑和思维之间的内在关系打开了一扇窗户。在本文中，我们回顾了大脑和语言网络中的一些结果，并说明了建模方法如何有助于理解大脑结构与其功能之间的关系。一方面，我们表明大脑和神经网络表现出具有最优复杂性的动态行为，这是它们同时分离和整合信息的能力之间的平衡的结果。另一方面，我们展示了神经组织的原则如何可以被实现到记忆存储和识别的模型中，以再现类似于心理语言学实验中研究的记忆联想现象的记忆之间的自发转换。
