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首页> 外文期刊>International Journal of Neural Systems >THE ROLE OF DOPAMINE IN THE MAINTENANCE OF WORKING MEMORY IN PREFRONTAL CORTEX NEURONS: INPUT-DRIVEN VERSUS INTERNALLY-DRIVEN NETWORKS
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THE ROLE OF DOPAMINE IN THE MAINTENANCE OF WORKING MEMORY IN PREFRONTAL CORTEX NEURONS: INPUT-DRIVEN VERSUS INTERNALLY-DRIVEN NETWORKS

机译:多巴胺在大脑皮质神经元工作记忆中的作用:输入驱动与内部驱动网络

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摘要

How do organisms select and organize relevant sensory input in working memory (WM) in order to deal with constantly changing environmental cues? Once information has been stored in WM, how is it protected from and altered by the continuous stream of sensory input and internally generated planning? The present study proposes a novel role for dopamine (DA) in the maintenance of WM in the prefrontal cortex (Pfc) neurons that begins to address these issues. In particular, DA mediates the alternation of the Pfc network between input-driven and internally-driven states, which in turn drives WM updates and storage. A biologically inspired neural network model of Pfc is formulated to provide a link between the mechanisms of state switching and the biophysical properties of Pfc neurons. This model belongs to the recurrent competitive fields33 class of dynamical systems which have been extensively mathematically characterized and exhibit the two functional states of interest: input-driven and internally-driven. This hypothesis was tested with two working memory tasks of increasing difficulty: a simple working memory task and a delayed alternation task. The results suggest that optimal WM storage in spite of noise is achieved with a phasic DA input followed by a lower DA sustained activity. Hypo and hyper-dopaminergic activity that alter this ideal pattern lead to increased distractibility from non-relevant pattern and prolonged perseverations on presented patterns, respectively.
机译:有机体如何选择和组织工作记忆(WM)中的相关感觉输入,以应对不断变化的环境提示?信息存储在WM中之后,如何保护其免受连续不断的感官输入和内部生成的计划的影响并对其进行更改?本研究提出了多巴胺(DA)在维持前额叶皮层(Pfc)神经元WM中的新作用,从而开始解决这些问题。特别是,DA介导了Pfc网络在输入驱动状态和内部驱动状态之间的交替,进而驱动WM更新和存储。制定了Pfc的生物学启发式神经网络模型,以提供状态转换机制与Pfc神经元的生物物理特性之间的联系。该模型属于动态系统的递归竞争领域33类,该类已在数学上进行了广泛的表征,并展现出两个令人关注的功能状态:输入驱动和内部驱动。使用两个难度越来越大的工作记忆任务测试了该假设:一个简单的工作记忆任务和一个延迟的交替任务。结果表明,尽管有噪声,但最佳的WM存储是通过分阶段的DA输入和较低的DA持续活动实现的。改变这种理想模式的低功率和高多巴胺能活动分别导致与非相关模式的分散性增加,以及对所呈现模式的持续性持续时间延长。

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