Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.
Deptartment of Psychology, The University of Sheffield, Sheffield, United Kingdom.
PLoS Comput Biol. 2018 Apr 3;14(4):e1006033. doi: 10.1371/journal.pcbi.1006033. eCollection 2018 Apr.
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.
决策形成涉及许多大脑区域，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来描述其基本组成部分，该算法基于具有感觉皮层（MT）中那些统计信息的尖峰火车来做出决策。使用它来模拟随机点运动任务，我们证明它可以定量复制猴子的选择行为，同时预测来自 MT 的否则可用信息的损失。其架构映射到皮质基底神经节丘脑皮质回路的递归，其组件都与决策相关。我们表明，其映射计算的动态与感觉运动皮层和纹状体在决策过程中的神经活动相匹配，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和不是推理的一部分。我们的单方程算法是概率的、分布式的、递归的和并行的。它在捕获解剖、行为和电生理学方面的成功表明，大脑所实现的机制具有这些相同的特征。
