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.项目总结 感知决策是基于由神经元群体编码的感觉信息。 短时间内。为了确定指导行为的人口准则中最重要的方面，我提出了各种 视觉注意力，它改善了对被关注的位置或特征的感知。信息量 在神经元群体中的编码受到单个皮质神经元的变异性和 在整个群体中共享的相关变异性(噪声相关性)。我发现这是一种专注 共享可变性的变化可能是导致行为改善的主要因素。 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遗漏了最多 神经元群体的反应引导行为方式的关键方面，因此有必要 监测神经元群体的活动，而不是一次记录一个神经元。 这项提议的目标是使用计算方法和多电极记录来唤醒 灵长类动物了解神经元间联系产生的机制和程度 他们可以灵活地适应任务需求。在我之前的实验中，我发现注意力调制 放电率和噪声的相关性是有联系的：表现出最大放电率变化的神经元 (通常是乘性增益的增加)显示相关性的最大下降。我将使用 本奖项的指导阶段，通过创建一个 一种计算模型来测试作为单个神经元增益基础的机制是否会发生变化 许多感觉、运动和认知过程(感觉正常化)也可能导致 噪声相关性(目标1)。相关性调制伴随着所有增益变化的结果 意味着相关性变化是大多数大脑皮层计算中的一个主要因素。 我在独立阶段的目标将是通过实验研究如何灵活和适应 相互关系的改变可能会影响大脑皮层之间的交流。 噪声相关性可以严重限制或改善神经元群体中可用的信息， 这取决于神经元反应的组合方式。在我以前使用的任务中 实验表明，噪声相关性限制了群体的敏感度，注意力自适应地降低 相关性(如归一化假设所预测的)。在目标2中，我将使用一个有噪音的任务 相关性改善而不是限制了表现，以决定注意力是否可以自适应地 增加关联性。在目标3中，我将通过测量相关性来调查噪声相关性的来源 不同皮质区域和不同注意力之间的关系，以确定相关性是否取决于 共享功能输入的强度。总的来说，这些研究将对以下影响产生影响 种群编码和感觉信息从一个区域传递到另一个区域的方式的相关性 用来指导行为的。