Tao Louis, Sornborger Andrew T
Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetics Engineering, College of Life Sciences, Peking University, Number 5 Summer Palace Road, Beijing 100871, People's Republic of China.
J Comput Neurosci. 2010 Feb;28(1):91-106. doi: 10.1007/s10827-009-0189-8. Epub 2009 Oct 6.
Systems-level neurophysiological data reveal coherent activity that is distributed across large regions of cortex. This activity is often thought of as an emergent property of recurrently connected networks. The fact that this activity is coherent means that populations of neurons may be thought of as the carriers of information, not individual neurons. Therefore, systems-level descriptions of functional activity in the network often find their simplest form as combinations of the underlying neuronal variables. In this paper, we provide a general framework for constructing low-dimensional dynamical systems that capture the essential systems-level information contained in large-scale networks of neurons. We demonstrate that these dimensionally-reduced models are capable of predicting the response to previously un-encountered input and that the coupling between systems-level variables can be used to reconstruct cellular-level functional connectivities. Furthermore, we show that these models may be constructed even in the absence of complete information about the underlying network.
系统层面的神经生理学数据揭示了分布在大脑皮层大片区域的连贯活动。这种活动通常被认为是循环连接网络的一种涌现属性。这种活动具有连贯性这一事实意味着神经元群体可被视为信息的载体，而非单个神经元。因此，网络中功能活动的系统层面描述通常以基础神经元变量的组合形式呈现出最简单的形式。在本文中，我们提供了一个通用框架，用于构建低维动力系统，该系统能够捕捉包含在大规模神经元网络中的基本系统层面信息。我们证明，这些降维模型能够预测对先前未遇到的输入的响应，并且系统层面变量之间的耦合可用于重建细胞层面的功能连接。此外，我们表明，即使在缺乏关于基础网络的完整信息的情况下，也可以构建这些模型。
