A link between neuroscience and informatics: large-scale modeling of memory processes.
A link between neuroscience and informatics: large-scale modeling of memory processes.
复制标题
神经科学和信息学之间的联系：记忆过程的大规模建模。
DOI：
10.1016/j.ymeth.2007.02.007
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Smith,JasonF
中科院分区：
文献类型：
--
作者：
Horwitz,Barry;Smith,JasonF
利用功能神经成像和计算神经建模的进展，神经科学家越来越多地寻求研究由功能定义的子区域组成的分布式网络如何结合联合收割机来产生认知。大规模的，生物学上真实的神经模型，整合了来自细胞，区域，整个大脑和行为来源的数据，描绘了关于这些相互作用的神经群体如何执行高级认知任务的具体假设。在这篇综述中，我们讨论了神经成像，神经建模，以及大规模的生物现实模型的效用，以短期记忆建模为例。我们提出了一个草图的数据，从非人类的电生理，计算和神经成像的角度来看，短期记忆的神经基础，突出了多个相互作用的大脑区域被认为是参与。通过回顾包括我们自己在内的几项努力，结合联合收割机神经建模和神经成像数据，我们认为大规模神经模型在理解认知和行为的分布式网络方面具有特定的优势。
Utilizing advances in functional neuroimaging and computational neural modeling, neuroscientists have increasingly sought to investigate how distributed networks, composed of functionally defined subregions, combine to produce cognition. Large-scale, biologically realistic neural models, which integrate data from cellular, regional, whole brain, and behavioral sources, delineate specific hypotheses about how these interacting neural populations might carry out high-level cognitive tasks. In this review, we discuss neuroimaging, neural modeling, and the utility of large-scale biologically realistic models using modeling of short-term memory as an example. We present a sketch of the data regarding the neural basis of short-term memory from non-human electrophysiological, computational and neuroimaging perspectives, highlighting the multiple interacting brain regions believed to be involved. Through a review of several efforts, including our own, to combine neural modeling and neuroimaging data, we argue that large scale neural models provide specific advantages in understanding the distributed networks underlying cognition and behavior.