Probabilistic coding in cortical populations

项目总结 大脑表现出的最令人印象深刻的壮举之一是它使用充满不确定性的信息来 成功地引导行为。世界充满了模棱两可的刺激，通过嘈杂的传感器感知，这是 安全。然而，大脑是否以及如何利用这种不确定性是一个开放而重要的问题。 使用有噪音或模糊刺激的任务的行为研究表明，受试者的表现几乎是 最好的。这些结果强烈地表明，大脑必须代表和使用有关感觉的信息 除了关于刺激参数的信息之外，不确定性本身，还有许多关于神经的理论 编码不考虑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”胜过刺激。PPC进一步提供了神经上可信的机制来执行 贝叶斯计算的次数，然而，这一有希望的理论从未经过神经生理学测试 在人口层面上。 研究大脑表达和使用感觉不确定性来引导适应性的机制 行为，目前的提议将结合多电极记录，计算神经科学和 心理物理学。具体地说，我们将研究定向分类任务中的视觉决策，我们 以前已经表明灵长类动物的表现接近最佳，这需要使用不确定性 信息，以实现最佳性能。在目标1中，我们将检验这样的假设：神经元群体 联合编码PPC预测的似然函数，通过在受试者执行 分类任务。在目标2中，我们将测试V1和V1之间对不确定信息的共享编码 前额叶皮质(PFC)导致了这两个区域之间的功能联系。通过我们的组合 在活体种群中记录清醒的、行为正常的灵长类动物，拟议的项目处于强有力的地位来测试 PPC背后的核心假说，并阐明大脑使最佳状态成为可能的机制 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.