Using Attention to Understand Cortical Population Codes

利用注意力来理解皮质群体代码

• 批准号: 8328684
• 负责人: Marlene Rochelle Cohen
• 金额: $ 24.78万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8328684
• 关键词: Accounting; Affect; Afferent Neurons; Animals; Area; Attention; Attention deficit hyperactivity disorder; Attentional deficit; Award; Behavior; Behavioral; Child; Code; Cognitive; Communication; Computer Simulation; Computing Methodologies; Data; Diagnosis; Discrimination; Disease; Goals; Human; Individual; Link; Location; Measures; Mental Depression; Mentors; Modeling; Monitor; Motor; Neurons; Neurosciences; Noise; Perception; Performance; Pharmacotherapy; Phase; Population; Primates; Process; Psychophysiology; Reading; Schizophrenia; Sensory; Sensory Process; Staging; Stimulus; Sum; System; Testing; Time; Training; United States; Visual attention; attentional modulation; awake; base; extrastriate visual cortex; flexibility; improved; nervous system disorder; neuromechanism; neurophysiology; novel; research study; response; tool

4. Project Summary Perceptual decisions are based on sensory information encoded by populations of neurons over short periods. To identify the most important aspects of the population code for guiding behavior, I varied visual attention, which improves perception of an attended location or feature. The amount of information encoded in a population of neurons is limited by both the variability of individual cortical neurons and by correlated variability that is shared across the population (noise correlations). I found that attentional changes in shared variability are likely the major contributor to the resulting behavioral improvement. These results suggest that studies of single neurons that ignore noise correlations miss perhaps the most crucial aspect of the way that responses of populations of neurons guide behavior, so it is necessary to monitor the activity of populations of neurons rather than record one neuron at a time. The goal of this proposal is to use computational methods and multielectrode recordings in awake primates to understand the mechanism by which inter-neuronal correlations arise and the extent to which they can flexibly adapt to task demands. In my previous experiments I found that attentional modulation of firing rates and noise correlations is linked: the neurons that show the biggest firing rate changes (typically increases in multiplicative gain) show the biggest decreases in correlation. I will use the mentored phase of this award to investigate the origin and flexibility of noise correlations by creating a computational model to test whether a mechanism thought to underlie gain changes in single neurons in many sensory, motor and cognitive processes (sensory normalization) could also cause modulation of noise correlations (Aim 1). The result that modulation of correlations accompanies all gain changes would imply that correlation changes are a major factor in most cortical computations. My goal in the independent phase will be to experimentally investigate how flexible and adaptive correlation changes can be and the impact of correlations on communication between cortical areas. Noise correlations can either severely limit or improve the information available in a neuronal population, depending on the way that neuronal responses are combined. In the task I used in my previous experiments, noise correlations limited population sensitivity, and attention adaptively decreased correlations (as predicted by the normalization hypothesis). In Aim 2, I will use a task in which noise correlations improve, rather than limit, performance to determine whether attention can adaptively increase correlations. In Aim 3, I will investigate the origin of noise correlations by measuring correlations between different cortical areas and varying attention to determine whether correlations depend on the strength of shared functional inputs. Collectively, these studies will have implications for the impact of correlations on population coding and the way sensory information is transmitted from area to area and used to guide behavior.

4。项目摘要 感知决定基于由神经元种群编码的感官信息 短期。为了确定人口指导行为的最重要方面，我变化了 视觉关注，可以提高对所在地或特征的感知。信息量 在神经元种群中编码的神经元受到了单个皮质神经元的变异性的限制和 在整个种群中共享的相关变异性（噪声相关）。我发现那个注意力 共享变异性的变化可能是导致行为改善的主要贡献者。 这些结果表明，对忽略噪声相关性的单个神经元的研究可能会错过最多 神经元种群反应指导行为的方式的关键方面，因此有必要 监测神经元种群的活性，而不是一次记录一个神经元。 该建议的目的是在清醒中使用计算方法和多电极记录 灵长类动物理解出现神经元相关性的机制以及多大程度的程度 他们可以灵活地适应任务需求。在以前的实验中，我发现注意调制 发射速率和噪声相关性的关联是链接的：显示最大发射速率变化的神经元 （通常增加乘法增益）显示相关的最大减少。我将使用 该奖项的指导阶段是通过创建一个来调查噪声相关的起源和灵活性 计算模型以测试一种考虑在单个神经元中增加增益变化的机制是否是 许多感觉，运动和认知过程（感觉归一化）也可能导致调制 噪声相关性（AIM 1）。相关调制伴随所有增益变化的结果 将暗示相关变化是大多数皮质计算的主要因素。 我在独立阶段的目标是实验研究如何灵活和适应性 相关性的变化可能是以及相关性对皮质区域之间交流的影响。 噪声相关性可以严重限制或改善神经元种群中可用的信息， 取决于神经元反应的组合方式。在我以前使用的任务中 实验，噪声相关性有限的人口敏感性，注意力适应降低 相关性（如归一化假设所预测）。在AIM 2中，我将使用一个噪音的任务 相关性改善而不是限制性能，以确定注意力是否可以适应 增加相关性。在AIM 3中，我将通过测量相关性研究噪声相关的起源 在不同的皮质区域之间以及注意力不同以确定相关性是否取决于 共享功能输入的强度。总的来说，这些研究将对 人口编码的相关性以及感官信息从区域之间传输的方式以及 用于指导行为。