University of Geneva, Département des neurosciences fondamentales, Geneva, Switzerland.
Albert Einstein College of Medicine, Bronx, Department of Systems & Computational Biology & Department of Neuroscience, New York, United States of America.
PLoS Comput Biol. 2021 Feb 12;17(2):e1008138. doi: 10.1371/journal.pcbi.1008138. eCollection 2021 Feb.
Skilled behavior often displays signatures of Bayesian inference. In order for the brain to implement the required computations, neuronal activity must carry accurate information about the uncertainty of sensory inputs. Two major approaches have been proposed to study neuronal representations of uncertainty. The first one, the Bayesian decoding approach, aims primarily at decoding the posterior probability distribution of the stimulus from population activity using Bayes' rule, and indirectly yields uncertainty estimates as a by-product. The second one, which we call the correlational approach, searches for specific features of neuronal activity (such as tuning-curve width and maximum firing-rate) which correlate with uncertainty. To compare these two approaches, we derived a new normative model of sound source localization by Interaural Time Difference (ITD), that reproduces a wealth of behavioral and neural observations. We found that several features of neuronal activity correlated with uncertainty on average, but none provided an accurate estimate of uncertainty on a trial-by-trial basis, indicating that the correlational approach may not reliably identify which aspects of neuronal responses represent uncertainty. In contrast, the Bayesian decoding approach reveals that the activity pattern of the entire population was required to reconstruct the trial-to-trial posterior distribution with Bayes' rule. These results suggest that uncertainty is unlikely to be represented in a single feature of neuronal activity, and highlight the importance of using a Bayesian decoding approach when exploring the neural basis of uncertainty.
熟练的行为通常表现出贝叶斯推理的特征。为了使大脑能够执行所需的计算，神经元活动必须携带有关感官输入不确定性的准确信息。已经提出了两种主要的方法来研究神经元对不确定性的表示。第一种方法，贝叶斯解码方法，主要旨在使用贝叶斯规则从群体活动中解码刺激的后验概率分布，并间接地作为副产品得出不确定性估计。第二种方法，我们称之为相关方法，它搜索与不确定性相关的神经元活动的特定特征（例如调谐曲线宽度和最大放电率）。为了比较这两种方法，我们通过耳间时间差（ITD）推导出声源定位的新规范模型，该模型再现了大量的行为和神经观察结果。我们发现，神经元活动的几个特征平均与不确定性相关，但没有一个特征可以准确地估计每个试验的不确定性，这表明相关方法可能无法可靠地确定神经元反应的哪些方面代表不确定性。相比之下，贝叶斯解码方法表明，需要整个群体的活动模式才能根据贝叶斯规则重建每个试验的后验分布。这些结果表明，不确定性不太可能在神经元活动的单个特征中表示，并且强调了当探索不确定性的神经基础时使用贝叶斯解码方法的重要性。
