A probabilistic, distributed, recursive mechanism for decision-making in the brain.
A probabilistic, distributed, recursive mechanism for decision-making in the brain.
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DOI：
10.1371/journal.pcbi.1006033
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发表时间：
2018-04
影响因子：
4.3
通讯作者：
Gurney KN
中科院分区：
文献类型：
--
作者：
Caballero JA;Humphries MD;Gurney KN
决策的形成需要许多大脑区域，但它们共同执行的过程尚不清楚。在这里，我们使用一种新颖的递归贝叶斯算法作为框架来描述其基本组成，该算法根据尖峰序列和感觉皮层（MT）中的统计数据做出决策。使用它来模拟随机点运动任务，我们证明它可以定量地复制猴子的选择行为，同时预测 MT 中其他可用信息的丢失。其架构映射到循环皮质-基底神经节-丘脑-皮质环路，其组成部分都与决策有关。我们表明，其映射计算的动态与决策过程中感觉运动皮层和纹状体的神经活动相匹配，并预测基底神经节输出和丘脑的动态。这也预测了神经动力学的哪些方面是推理的一部分，哪些不是推理的一部分。我们的单方程算法是概率性、分布式、递归和并行的。它在捕捉解剖学、行为和电生理学方面的成功表明，大脑实现的机制具有这些相同的特征。决策是认知的核心。异常形成的决定是暴饮暴食、帕金森病和亨廷顿病、强迫症、成瘾和强迫性赌博等疾病的特征。然而，迄今为止，对决策的统一解释仍然难以实现。在这里，我们通过将猴子做出决策的实验数据与新型统计推理算法的可知函数相匹配，展示了大脑决策机制的基本组成。我们的算法映射到灵长类动物大脑中决策电路的大规模架构，复制猴子的选择行为以及伴随它的神经活动的动态。通过这种方式验证，我们的算法建立了一个基本框架，用于理解大脑决策的机制成分，从而为理解异常功能如何产生病理学提供了一个基本平台。
Decision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT). Using it to simulate the random-dot-motion task, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT. Its architecture maps to the recurrent cortico-basal-ganglia-thalamo-cortical loops, whose components are all implicated in decision-making. We show that the dynamics of its mapped computations match those of neural activity in the sensorimotor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus. This also predicts which aspects of neural dynamics are and are not part of inference. Our single-equation algorithm is probabilistic, distributed, recursive, and parallel. Its success at capturing anatomy, behaviour, and electrophysiology suggests that the mechanism implemented by the brain has these same characteristics. Decision-making is central to cognition. Abnormally-formed decisions characterize disorders like over-eating, Parkinson’s and Huntington’s diseases, OCD, addiction, and compulsive gambling. Yet, a unified account of decision-making has, hitherto, remained elusive. Here we show the essential composition of the brain’s decision mechanism by matching experimental data from monkeys making decisions, to the knowable function of a novel statistical inference algorithm. Our algorithm maps onto the large-scale architecture of decision circuits in the primate brain, replicating the monkeys’ choice behaviour and the dynamics of the neural activity that accompany it. Validated in this way, our algorithm establishes a basic framework for understanding the mechanistic ingredients of decision-making in the brain, and thereby, a basic platform for understanding how pathologies arise from abnormal function.
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影响因子：
16.2
作者：
Beck JM;Ma WJ;Pitkow X;Latham PE;Pouget A
通讯作者：
Pouget A
影响因子：
16.2
作者：
Ding L;Gold JI
通讯作者：
Gold JI
影响因子：
5.7
作者：
Churchland AK;Ditterich J
通讯作者：
Ditterich J
影响因子：
16.2
作者：
Beck JM;Ma WJ;Kiani R;Hanks T;Churchland AK;Roitman J;Shadlen MN;Latham PE;Pouget A
通讯作者：
Pouget A
影响因子：
3.7
作者：
Ding, Long;Gold, Joshua I.
通讯作者：
Gold, Joshua I.