Real-Time Population Coding Underlying Behavioral Decisions

虽然我们对单个神经元如何编码信息有很多了解，但细胞网络如何在群体活动中代表传入的刺激却很少进行实验研究。事实上，尽管改进的多电极技术和理论模型现在可以帮助回答这个重要的问题，但皮层网络如何编码信息来影响行为反应的准确性仍然未知。我们将采用最先进的电生理和行为技术，结合网络功能的计算模型，同时记录行为猴的多个视觉皮质区域的多个神经元的活动，以了解神经回路如何产生紧急特性。我们计划首次研究不同皮质区域的神经元群体如何编码信息以影响行为决策。当猴子执行图像识别任务时，我们将进行高难度的实验，同时记录沿同一皮质处理流（例如V1， V4和IT；下颞叶皮层，或IT，是“物体”通路的终端处理站）的多个皮质区域的神经元活动。这些实验将为我们提供独特的机会来检查视觉信息在视觉处理的每个阶段的编码和不同皮层区域之间的信息流，从而研究整个参与图像处理的网络如何工作，以创建和更新与行为相关的视觉表征。实验还将使我们第一次检查不同类型的细胞（例如，兴奋性和抑制性）对种群编码的贡献及其对行为表现的影响。我们将首次研究在多个皮层区域的人口密码的性质和行为决策之间的关系。我们将通过在图像识别任务中同时记录沿同一皮层处理流的几个皮质区域的神经元活动来进行高难度的实验。这些研究将为我们提供独特的机会来研究多个神经网络之间的相互作用如何影响行为决策。

Although we know a great deal about how individual neurons encode information, how networks of cells represent incoming stimuli in population activity has rarely been investigated experimentally. Indeed, although improved multi-electrode techniques and theoretical models are now available to help answer this important question, exactly how cortical networks encode information to influence the accuracy of behavioral responses remains unknown. We will employ state-of-the-art electrophysiological and behavioral techniques to record simultaneously the activity of multiple neurons in multiple visual cortical areas of behaving monkey in combination with computational models of network function to understand how neural circuits produce emergent properties. We plan to examine for the first time how populations of neurons in different cortical areas encode information to influence behavioral decisions. We will perform experiments of a high degree of difficulty to simultaneously record neuronal activity in multiple cortical areas along the same cortical processing stream (e.g., V1, V4, and IT; infero-temporal cortex, or IT, is the terminal processing station of the 'object' pathway) while monkeys will perform an image discrimination task. These experiments will offer us the unique opportunity to examine the coding of visual information at each stage of visual processing and the flow of information between different cortical areas, and thus investigate how the entire network engaged in image processing works to create and update visual representations that are relevant for behavior. The experiments will also allow us to examine for the first time the contribution of different types of cells (e.g., excitatory and inhibitory) to population coding and their impact on behavioral performance. We will examine for the first time the relationship between the properties of the population code in multiple cortical areas and behavioral decisions. We will perform experiments of a high degree of difficulty by simultaneously record neuronal activity in several cortical areas along the same cortical processing stream during an image discrimination task. These studies will offer us the unique opportunity to investigate how interactions between multiple neuronal networks influence behavioral decisions.