Department of Informatics, University of Sussex, Brighton, BN1 9QJ, UK,
Cogn Neurodyn. 2008 Mar;2(1):49-64. doi: 10.1007/s11571-007-9031-z. Epub 2007 Oct 20.
Neurons engage in causal interactions with one another and with the surrounding body and environment. Neural systems can therefore be analyzed in terms of causal networks, without assumptions about information processing, neural coding, and the like. Here, we review a series of studies analyzing causal networks in simulated neural systems using a combination of Granger causality analysis and graph theory. Analysis of a simple target-fixation model shows that causal networks provide intuitive representations of neural dynamics during behavior which can be validated by lesion experiments. Extension of the approach to a neurorobotic model of the hippocampus and surrounding areas identifies shifting causal pathways during learning of a spatial navigation task. Analysis of causal interactions at the population level in the model shows that behavioral learning is accompanied by selection of specific causal pathways-"causal cores"-from among large and variable repertoires of neuronal interactions. Finally, we argue that a causal network perspective may be useful for characterizing the complex neural dynamics underlying consciousness.
神经元彼此之间以及与周围的身体和环境之间存在因果相互作用。因此，可以根据因果网络来分析神经网络，而无需对信息处理、神经编码等进行假设。在这里，我们回顾了一系列使用格兰杰因果分析和图论相结合的方法来分析模拟神经网络中的因果网络的研究。对一个简单的目标注视模型的分析表明，因果网络提供了行为期间神经动力学的直观表示，可以通过损伤实验来验证。该方法扩展到海马体及其周围区域的神经机器人模型，确定了在空间导航任务学习过程中因果途径的转移。对模型中群体水平上因果相互作用的分析表明，行为学习伴随着从神经元相互作用的大量和可变的组合中选择特定的因果途径——“因果核心”。最后，我们认为，因果网络的观点可能有助于描述意识背后复杂的神经动力学。
