Neural substrate of dynamic Bayesian inference in the cerebral cortex
Neural substrate of dynamic Bayesian inference in the cerebral cortex
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DOI：
10.1038/nn.4390
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发表时间：
2016-12-01
影响因子：
25
通讯作者：
Doya, Kenji
中科院分区：
文献类型：
--
作者：
Funamizu, Akihiro;Kuhn, Bernd;Doya, Kenji
动态贝叶斯推理允许系统在有限的感官观察条件下推断环境状态。使用一个目标达成任务，我们发现，后顶叶皮层（PPC）和相邻的后内侧皮层（PM）实现了动态贝叶斯推理的两个基本特征：使用内部状态转换模型预测隐藏状态和更新预测新的感官证据。我们在声学虚拟现实系统中光学成像小鼠PPC和PM层2，3和5中的神经元活动。当小鼠接近奖励网站，预期舔增加，即使声音提示间歇性地提出，这是由PPC沉默干扰。概率群体解码显示，PPC和PM中的神经元在声音省略（预测）期间代表目标距离，特别是在PPC层3和5中，并且随着对提示声音的观察（更新），预测得到改善。我们的研究结果说明了大脑皮层如何实现心理模拟的动作依赖的动态模型。
Dynamic Bayesian inference allows a system to infer the environmental state under conditions of limited sensory observation. Using a goal-reaching task, we found that posterior parietal cortex (PPC) and adjacent posteromedial cortex (PM) implemented the two fundamental features of dynamic Bayesian inference: prediction of hidden states using an internal state transition model and updating the prediction with new sensory evidence. We optically imaged the activity of neurons in mouse PPC and PM layers 2, 3 and 5 in an acoustic virtual-reality system. As mice approached a reward site, anticipatory licking increased even when sound cues were intermittently presented; this was disturbed by PPC silencing. Probabilistic population decoding revealed that neurons in PPC and PM represented goal distances during sound omission (prediction), particularly in PPC layers 3 and 5, and prediction improved with the observation of cue sounds (updating). Our results illustrate how cerebral cortex realizes mental simulation using an action-dependent dynamic model.