Multiscale Investigation of Neural Circuitry of Visual Cognition in Primates

大脑是如何引导注意力和监控错误从而控制行为的，这两项研究分别在人类和猴子身上进行了研究，分别采用脑电图（EEG）等非侵入性脑功能测量和脑细胞（神经元）放电和突触局部场电位的侵入性测量。我们知道脑电图是由大脑产生的。但是，使用头部的脑电图来定位大脑中的来源（称为逆问题）可以提供多种解决方案。但是从大脑的特定来源获取脑电图（称为前向问题）有一个解决方案。该研究项目通过在人类脑电图发现和猴子大脑功能之间建立桥梁，寻求人类注意力和监测的大脑机制。我们的研究已经证实猴子和人类一样有脑电图。我们获得了一个全面而独特的数据集，包括同时测量猕猴在执行需要视觉注意和错误监测的任务时大脑皮层四个区域的脑电图、放电和场电位。两个区域由典型的6个皮质层组成。两个缺少致密层。需要资金来描述在这4个区域的皮层中发现的信号的多样性，然后确定这些大脑信号是如何产生脑电图信号的。我们将先进的生物物理理论、复杂的数学方法和易于处理的单个神经元模型应用于具有独特行为相关性、大脑区域多样性和理解人类过程相关性的数据集，从而产生了对可行性的高度信心。了解脑回路如何产生EEG测量将解决一个长期存在的问题，在两个研究领域之间建立经验桥梁，并改进脑机接口技术。了解大脑如何引导注意力和监控错误，将解决有关人类感知和表现的相互竞争的理论，并转化为更有效的机器视觉和机器人系统。

How the brain guides attention and monitors errors to govern behavior has been investigated in humans with noninvasive measures of brain function like the electro-encephalogram (EEG) and in monkeys with invasive measurements of brain cell (neuron) discharges and synaptic local field potentials. We know that the EEG is produced by the brain. But, using the EEG on the head to locate sources in the brain (known as the inverse problem) allows multiple solutions. But deriving the EEG from particular sources in the brain (known as the forward problem) has one solution. This research program seeks the brain mechanisms of human attention and monitoring by building a bridge between human EEG findings and monkey brain function. Our research has verified that monkeys have EEG measures like humans. We have obtained a comprehensive and unique dataset consisting of simultaneous measurements of EEG with discharges and field potentials from four areas of the cerebral cortex of macaque monkeys performing tasks demanding visual attention and error monitoring. Two areas consist of the typical 6 cortical layers. Two lack the dense layer 4. Funds are requested to characterize the diversity of signals found across cortical layers in each of the 4 areas and then to determine how these brain signals produce the EEG signals. High confidence in feasibility is engendered by our application of advanced biophysical theory, sophisticated mathematical approaches, and tractable models of single neurons applied to a dataset that is unique in behavioral relevance, diversity of brain regions, and relevance for understanding human processes. Understanding how brain circuits produce EEG measures will solve a long-standing problem, establish an empirical bridge between two research domains, and improve brain-machine interface technology. Understanding how the brain guides attention and monitors errors will resolve competing theories of human perception and performance and translate into more effective machine vision and robotic systems.