Probabilistic coding in cortical populations

项目摘要 大脑表现出的最令人印象深刻的能力之一是它利用充满不确定性的信息， 成功引导行为。世界充满了模糊的刺激，通过嘈杂的传感器感知， 清楚然而，大脑是否以及如何利用这种不确定性是一个开放和重要的问题。 行为研究使用的任务与嘈杂或模糊的刺激表明，受试者的表现几乎是 最佳这些结果有力地表明，大脑必须代表和使用有关感官的信息 不确定性本身除了有关刺激参数的信息，但许多关于神经系统的理论， 编码没有说明如何表示或使用这种不确定性。概率论 群体编码（PPC）确实提供了这样的解释，声称神经元群体编码 刺激参数的估计和关于这些估计的不确定性，同时以 “似然函数”。PPC还提供了神经上合理的机制，用于执行一个 然而，尽管贝叶斯计算的数量很多，但这个有前途的理论从未经过神经生理学的测试 在人口水平上。 研究大脑代表和使用感官不确定性来指导适应性的机制。 行为，目前的建议将结合联合收割机多电极记录，计算神经科学和 心理物理学具体来说，我们将研究视觉决策的方向分类任务，我们 我之前已经证明灵长类动物的表现接近最佳状态，这需要使用不确定性 信息，以实现最佳性能。在目标1中，我们将检验神经元群体 联合编码由PPC预测的似然函数，通过从V1记录，同时受试者执行 分类任务。在目标2中，我们将测试V1和V2之间的不确定性信息的共享编码是否是一个问题。 前额叶皮层（PFC）导致这两个区域之间的功能相关性。通过我们的结合， 在清醒的，行为灵长类动物的体内种群记录中，所提出的项目被强烈定位于测试 PPC背后的核心假设，并阐明大脑使最佳的机制， 在嘈杂环境中的行为表现。

PROJECT SUMMARY One of the most impressive feats the brain performs is its use of information that is rife with uncertainty to successfully guide behavior. That the world is full of ambiguous stimuli and perceived through noisy sensors is clear. However, whether and how the brain makes use of this uncertainty is an open and important question. Behavioral studies using tasks with noisy or ambiguous stimuli suggest that subjects' performance is nearly optimal. These results strongly suggest that the brain must represent and use information about sensory uncertainty itself in addition to information about parameters of the stimulus, yet many theories about neural coding do not account for how such uncertainty could be represented or used. The theory of Probabilistic Population Coding (PPC) does provide such an account, claiming that populations of neurons encode estimates of stimulus parameters and uncertainty regarding those estimates simultaneously in the form of a “likelihood function” over the stimulus. PPCs further provide neurally plausible mechanisms for performing a number of Bayesian computations, however, this promising theory has never been tested neurophysiologically at the population level. To study the mechanisms by which the brain represents and uses sensory uncertainty to guide adaptive behavior, the current proposal will combine multi-electrode recordings, computational neuroscience and psychophysics. Specifically, we will study visual decision-making in an orientation classification task, which we have previously shown that primates perform near optimally and which requires the use of uncertainty information to achieve optimal performance. In Aim 1, we will test the hypothesis that populations of neurons jointly encode likelihood functions as predicted by PPC, by recording from V1 while subjects perform the classification task. In Aim 2, we will test whether a shared encoding of uncertainty information between V1 and prefrontal cortex (PFC) leads to a functional correlation between these two areas. Through our combination of in vivo population recordings in awake, behaving primates, the proposed project is strongly positioned to test the core hypothesis behind PPC and elucidate the mechanisms by which the brain makes possible optimal behavioral performance in a noisy environment.