Dynamics and plasticity of stimulus-selective persistent activity in cortical network models
Dynamics and plasticity of stimulus-selective persistent activity in cortical network models
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DOI：
10.1093/cercor/bhg096
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发表时间：
2003-11-01
期刊：
影响因子：
3.7
通讯作者：
Brunel, N
中科院分区：
文献类型：
--
作者：
Brunel, N
持续的神经元活动广泛存在于猴子大脑皮层的许多区域，这些猴子执行具有工作记忆成分的认知任务。建模研究有助于理解在皮层回路中持续活动的条件。在这里，我们首先回顾了几个基本的模型的持续活动，包括ESTA模型与激励只有和多稳态模型的工作记忆的一组离散的图片或对象的结构化的激励和全球抑制。在许多实验中，持续的活动已被证明是由于联想学习的变化。在皮层网络模型中，赫布学习塑造了突触结构，反过来，当图片在任务过程中关联在一起时，持续活动的属性。它示出了如何的理论模型可以重现基本的实验结果的神经生理记录从下颞叶和嗅周皮质获得使用以下实验协议：（i）对关联任务;（ii）对关联任务与颜色开关;和（iii）延迟匹配到样本任务与固定序列的样本。
Persistent neuronal activity is widespread in many areas of the cerebral cortex of monkeys performing cognitive tasks with a working memory component. Modeling studies have helped understanding of the conditions under which persistent activity can be sustained in cortical circuits. Here, we first review several basic models of persistent activity, including bistable models with excitation only and multistable models for working memory of a discrete set of pictures or objects with structured excitation and global inhibition. In many experiments, persistent activity has been shown to be subject to changes due to associative learning. In cortical network models, Hebbian learning shapes the synaptic structure and, in turn, the properties of persistent activity when pictures are associated together in the course of a task. It is shown how the theoretical models can reproduce basic experimental findings of neurophysiological recordings from inferior temporal and perirhinal cortices obtained using the following experimental protocols: (i) the pair-associate task; (ii) the pair-associate task with color switch; and (iii) the delay match to sample task with a fixed sequence of samples.