Representation and propagation of uncertainty information in cortical populations

 描述(由申请人提供)：了解大脑用来表示和计算不确定信息的机制标志着系统神经科学中的一项基本探索。由于传感器的噪声和世界上的模糊性，生物体经常根据观察结果做出决定，这些观察结果本身就是不确定的。为了以最佳方式完成这类任务，大脑有必要表示和利用有关感觉不确定性的知识。行为学研究表明，在某些任务中，人类的表现接近最佳，这意味着大脑在逐一试验的基础上表现和利用不确定性。概率群体编码(PPC)的理论框架假设，大脑通过在刺激上表示“似然函数”来编码群体活动模式中的感觉信息。尽管PPC已经被用来使用神经上看似合理的操作来构建几个贝叶斯计算的实现，但还没有种群水平的生理学证据表明这是大脑使用的编码方案。拟议的项目结合了电生理学(多神经元记录)和计算神经科学，目的是阐明大脑在视觉决策过程中如何在感觉不确定的情况下表示和计算。该项目将使用我们实验室先前设计的一个简单的定向分类任务。在这项任务上的最佳表现需要观察者在逐一试验的基础上利用感觉上的不确定性，而人类和猴子已经被证明表现得接近最佳。在目标1中，初级视觉皮质的群体记录将与行为测量相结合，以确定感觉不确定性是否在该区域被编码，具体地说，作为符合PPC的似然函数。在目标2中，前额叶皮层的同步记录将被用来检验由于共享不确定性信息编码，V1和前额叶皮质之间是否存在功能关联。综上所述，拟议的工作将是迄今为止贝叶斯计算在感知决策方面最全面的测试，特别关注PPC作为待测试的领先编码框架。了解大脑如何实现感觉不确定性有望迎来新一代神经网络，可能应用于开发从大脑皮层群体中改进解码的神经假体设备。

 DESCRIPTION (provided by applicant): Understanding the mechanisms used by the brain to represent, and compute with, uncertain information marks a fundamental quest in systems neuroscience. Organisms often make decisions based on observations that are inherently uncertain due to noisy sensors and ambiguity in the world. To optimally perform such tasks, it is necessary for the brain to represent and utilize knowledge of sensory uncertainty. Behavioral studies have demonstrated that in certain tasks, humans perform close to optimally, implying that the brain represents and utilize uncertainty on a trial-by-trial basis. The theoretical framework of probabilistic population coding (PPC) postulates that the brain encodes sensory information in the pattern of population activity by representing a "likelihood function" over the stimulus. Although PPCs have been used to construct implementations of several Bayesian computations using neurally plausible operations, there has been no population-level physiological evidence that this is the coding scheme used by the brain. The proposed project combines electrophysiology (multi-neuronal recordings) and computational neuroscience with the goal to elucidate how the brain represents and computes with sensory uncertainty during visual decision-making. The project will use a simple orientation classification task previously designed in our laboratories. Optimal performance on this task requires the observer to utilize sensory uncertainty on trial-by- trial basis, and human and monkeys have been shown to perform near optimally. In Aim 1, population recordings in primary visual cortex will be combined with behavioral measurements to determine whether sensory uncertainty is encoded in this area, specifically, as likelihood functions in accordance to PPC. In Aim 2, simultaneous recordings from prefrontal cortex will be used to test whether there exists functional correlation between V1 and prefrontal cortex due to shared uncertainty information encoding. Taken together, the proposed work will be the most comprehensive test to date of Bayesian computation in perceptual decision- making, with a particular focus on PPC as the leading encoding framework to be tested. Understanding how the brain implements sensory uncertainty promises to usher in a new generation of neural networks, with possible applications to developing neuroprosthetic devices with improved decoding from cortical populations.