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基底神经节决策回路的神经网络模型。

A neural network model of basal ganglia's decision-making circuitry.

作者信息

Chen Xiyuan, Yang Tianming

机构信息

Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, System Neuroscience Building, Rm 302, 320 Yueyang Rd, Shanghai, China.

University of Chinese Academy of Sciences, Beijing, 100049 China.

出版信息

Cogn Neurodyn. 2021 Feb;15(1):17-26. doi: 10.1007/s11571-020-09609-2. Epub 2020 Jun 24.

Abstract

The basal ganglia have been increasingly recognized as an important structure involved in decision making. Neurons in the basal ganglia were found to reflect the evidence accumulation process during decision making. However, it is not well understood how the direct and indirect pathways of the basal ganglia work together for decision making. Here, we create a recurrent neural network model that is composed of the direct and indirect pathways and test it with the classic random dot motion discrimination task. The direct pathway drives the outputs, which are modulated through a gating mechanism controlled by the indirect pathway. We train the network to learn the task and find that the network reproduces the accuracy and reaction time patterns of previous animal studies. Units in the model exhibit ramping activities that reflect evidence accumulation. Finally, we simulate manipulations of the direct and indirect pathways and find that the manipulations of the direct pathway mainly affect the choice while the manipulations of the indirect pathway affect the model's reaction time. These results suggest a potential circuitry mechanism of the basal ganglia's role in decision making with predictions that can be tested experimentally in the future.

摘要

基底神经节越来越被认为是参与决策的重要结构。研究发现,基底神经节中的神经元能够反映决策过程中的证据积累过程。然而,目前尚不清楚基底神经节的直接通路和间接通路如何协同工作以进行决策。在此,我们创建了一个由直接通路和间接通路组成的循环神经网络模型,并使用经典的随机点运动辨别任务对其进行测试。直接通路驱动输出,该输出通过由间接通路控制的门控机制进行调节。我们训练该网络学习任务,发现该网络再现了先前动物研究中的准确性和反应时间模式。模型中的单元表现出反映证据积累的斜坡活动。最后,我们模拟了对直接通路和间接通路的操作,发现对直接通路的操作主要影响选择,而对间接通路的操作影响模型的反应时间。这些结果表明了基底神经节在决策中作用的潜在电路机制,并提出了可在未来通过实验进行测试的预测。

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