A network model of basal ganglia for understanding the roles of dopamine and serotonin in reward-punishment-risk based decision making.
A network model of basal ganglia for understanding the roles of dopamine and serotonin in reward-punishment-risk based decision making.
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DOI：
10.3389/fncom.2015.00076
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发表时间：
2015
影响因子：
3.2
通讯作者：
Moustafa AA
中科院分区：
文献类型：
--
作者：
Balasubramani PP;Chakravarthy VS;Ravindran B;Moustafa AA
有重要证据表明，除了基于奖惩的决策外，基底神经节（BG）还有助于基于风险的决策（Balasubramani等人）。尽管有这样的证据，很少有人知道的计算原则和神经相关的风险计算在这个皮层下系统。我们之前已经提出了一种基于强化学习（RL）的BG模型，该模型模拟了多巴胺（DA）和5-羟色胺（5 HT）之间的相互作用，这些相互作用是在一系列不同的实验研究中进行的，包括奖励、惩罚和基于风险的决策（Balasubramani等人）。从中脑DA的活动代表奖励预测误差的经典观点开始，该模型假设纹状体中的多巴胺能活动控制风险预测误差。我们先前的BG模型是一个抽象模型，没有纳入解剖学和细胞水平的数据。在这项工作中，我们将早期的模型扩展为BG的详细网络模型，并展示了DA-5 HT在风险和奖惩敏感性方面的联合贡献。所提出的网络模型的核心是以下关于价值和风险计算的细胞相关性的见解。正如DA D1受体（D1 R）表达的纹状体中型棘神经元（MSN）被认为是价值计算的神经基质，我们提出DA D1 R和D2 R共表达的MSN能够计算风险。尽管各种实验研究报道了共表达D1 R和D2 R的MSN的存在，但先前现有的计算模型并不包括它们。我们的模型是第一个解释这些共表达D1 R-D2 R MSNs的计算可能性的模型，并描述了DA和5 HT如何介导这些神经元（D1 R-，D2 R-，D1 R-D2 R- MSNs）的活动。从5 HT调节所有MSN的假设出发，我们的研究预测了5 HT对D2 R和共表达D1 R-D2 R的MSN的显著调节作用，这反过来解释了5 HT在BG中的多种功能。本研究模拟的实验将5 HT与风险敏感性和奖惩学习联系起来。此外，我们的模型被证明捕捉奖惩和风险为基础的决策障碍帕金森氏病（PD）。该模型预测，优化5-HT水平沿着DA药物可能对改善患者的奖励-惩罚学习缺陷至关重要。
There is significant evidence that in addition to reward-punishment based decision making, the Basal Ganglia (BG) contributes to risk-based decision making (Balasubramani et al.,). Despite this evidence, little is known about the computational principles and neural correlates of risk computation in this subcortical system. We have previously proposed a reinforcement learning (RL)-based model of the BG that simulates the interactions between dopamine (DA) and serotonin (5HT) in a diverse set of experimental studies including reward, punishment and risk based decision making (Balasubramani et al.,). Starting with the classical idea that the activity of mesencephalic DA represents reward prediction error, the model posits that serotoninergic activity in the striatum controls risk-prediction error. Our prior model of the BG was an abstract model that did not incorporate anatomical and cellular-level data. In this work, we expand the earlier model into a detailed network model of the BG and demonstrate the joint contributions of DA-5HT in risk and reward-punishment sensitivity. At the core of the proposed network model is the following insight regarding cellular correlates of value and risk computation. Just as DA D1 receptor (D1R) expressing medium spiny neurons (MSNs) of the striatum were thought to be the neural substrates for value computation, we propose that DA D1R and D2R co-expressing MSNs are capable of computing risk. Though the existence of MSNs that co-express D1R and D2R are reported by various experimental studies, prior existing computational models did not include them. Ours is the first model that accounts for the computational possibilities of these co-expressing D1R-D2R MSNs, and describes how DA and 5HT mediate activity in these classes of neurons (D1R-, D2R-, D1R-D2R- MSNs). Starting from the assumption that 5HT modulates all MSNs, our study predicts significant modulatory effects of 5HT on D2R and co-expressing D1R-D2R MSNs which in turn explains the multifarious functions of 5HT in the BG. The experiments simulated in the present study relates 5HT to risk sensitivity and reward-punishment learning. Furthermore, our model is shown to capture reward-punishment and risk based decision making impairment in Parkinson's Disease (PD). The model predicts that optimizing 5HT levels along with DA medications might be essential for improving the patients' reward-punishment learning deficits.
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影响因子：
2.9
作者：
Brown, Joshua W.;Braver, Todd S.
通讯作者：
Braver, Todd S.
影响因子：
5.4
作者：
BELL, DE
通讯作者：
BELL, DE
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作者：
Bodi, Nikoletta;Keri, Szabolcs;Gluck, Mark A.
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影响因子：
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作者：
Bedard, Catherine;Wallman, Marie-Josee;Parent, Martin
通讯作者：
Parent, Martin
影响因子：
3.2
作者：
Balasubramani PP;Chakravarthy VS;Ravindran B;Moustafa AA
通讯作者：
Moustafa AA