美国贝勒医学院Andreas S. Tolias和Edgar Y. Walker以及纽约大学Wei Ji Ma研究组合作建立视觉皮层中概率计算的神经基础。该研究2019年12月23日发表于国际学术期刊《自然—神经科学》。

贝叶斯行为模型表明，生物代表与感官变量相关的不确定性。但是，不确定性的神经编码仍然模糊不清。一个中心假设是不确定性以似然函数的形式被编码进皮质神经元的种群活动中。研究人员通过在视觉分类工作中同时记录来自灵长类动物视觉皮层的种群活动来检验该假设，在该工作中，有关刺激方向的试验到试验不确定性与该决策有关。他们从试验到试验的群体活动对似然函数进行解码，发现它比有方向的点评估更好地预测了决策。

据悉，当他们以正确的方向为条件时，情况仍然如此，这表明神经活动的内部波动驱动了似然函数的行为上有意义的变化。他们的结果建立了群体编码似然函数在中介行为中的作用，并为感知的贝叶斯模型提供了神经基础。

附：英文原文

Title: A neural basis of probabilistic computation in visual cortex

Author: Edgar Y. Walker, R. James Cotton, Wei Ji Ma, Andreas S. Tolias

Issue&Volume: 2019-12-23

Abstract: Bayesian models of behavior suggest that organisms represent uncertainty associated with sensory variables. However, the neural code of uncertainty remains elusive. A central hypothesis is that uncertainty is encoded in the population activity of cortical neurons in the form of likelihood functions. We tested this hypothesis by simultaneously recording population activity from primate visual cortex during a visual categorization task in which trial-to-trial uncertainty about stimulus orientation was relevant for the decision. We decoded the likelihood function from the trial-to-trial population activity and found that it predicted decisions better than a point estimate of orientation. This remained true when we conditioned on the true orientation, suggesting that internal fluctuations in neural activity drive behaviorally meaningful variations in the likelihood function. Our results establish the role of population-encoded likelihood functions in mediating behavior and provide a neural underpinning for Bayesian models of perception.

DOI: 10.1038/s41593-019-0554-5

Source: https://www.nature.com/articles/s41593-019-0554-5