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Probabilistic coding in cortical populations

皮质群体的概率编码

基本信息

批准号：
10133077
负责人：
Wei Ji Ma
金额：
$ 37.55万
依托单位：
BAYLOR COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2023-03-31
项目状态：
已结题

项目摘要

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.

项目概要大脑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成功引导行为。世界充满了模糊的刺激并通过嘈杂的传感器感知清除。然而，大脑是否以及如何利用这种不确定性是一个开放且重要的问题。使用带有噪音或模糊刺激的任务的行为研究表明，受试者的表现几乎与最佳的。这些结果强烈表明大脑必须代表和使用有关感觉的信息除了有关刺激参数的信息之外，不确定性本身也存在，但许多关于神经元的理论编码没有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刺激的“似然函数”。 PPC 进一步提供了神经上合理的机制来执行贝叶斯计算的数量，然而，这个有前途的理论从未经过神经生理学的测试在人口层面。研究大脑代表和使用感觉不确定性来指导适应性的机制行为，当前的提案将结合多电极记录、计算神经科学和心理物理学。具体来说，我们将研究方向分类任务中的视觉决策，我们之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，我们将检验以下假设：神经元群体通过在受试者执行以下操作时从 V1 记录，联合编码 PPC 预测的似然函数分类任务。在目标 2 中，我们将测试 V1 和 V1 之间是否共享不确定性信息的编码前额皮质（PFC）导致这两个区域之间存在功能相关性。通过我们的结合在清醒、有行为的灵长类动物体内进行群体记录，拟议的项目非常有利于测试 PPC 背后的核心假设，并阐明大脑使之成为可能的机制嘈杂环境中的行为表现。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The role of sensory uncertainty in simple contour integration.

DOI：
10.1371/journal.pcbi.1006308
发表时间：
2020-11
期刊：
PLoS computational biology
影响因子：
4.3
作者：
Zhou Y;Acerbi L;Ma WJ
通讯作者：
Ma WJ

Visuomotor control in mice and primates.

DOI：
10.1016/j.neubiorev.2021.08.009
发表时间：
2021-11
期刊：
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS
影响因子：
8.2
作者：
Tehovnik, E. J.;Froudarakis, E.;Scala, F.;Smirnakis, S. M.;Patel, S. S.;Tolias, A. S.
通讯作者：
Tolias, A. S.

A detailed comparison of optimality and simplicity in perceptual decision making.