Using Attention to Understand Cortical Population Codes

利用注意力来理解皮质群体代码

• 批准号: 8541019
• 负责人: Marlene Rochelle Cohen
• 金额: $ 23.4万
• 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 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8541019
• 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; Psychophysics; 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.

四、项目概要 感知决策基于神经元群体编码的感觉信息 短期。为了确定指导行为的人口准则中最重要的方面，我改变了 视觉注意力，提高对所关注位置或特征的感知。信息量 神经元群体中的编码受到单个皮层神经元的可变性和 整个群体共有的相关变异性（噪声相关性）。我发现关注 共同变异性的变化可能是最终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遗漏最重要的信息。 神经元群体反应指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。 该提案的目标是在清醒状态下使用计算方法和多电极记录 灵长类动物了解神经元间相关性产生的机制以及其程度 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节 放电率和噪声相关性是相关的：显示最大放电率变化的神经元 （通常是乘法增益的增加）显示相关性的最大下降。我将使用 该奖项的指导阶段是通过创建一个模型来研究噪声相关性的起源和灵活性 计算模型来测试是否有一种机制被认为是单个神经元增益变化的基础 许多感觉、运动和认知过程（感觉正常化）也可能导致调节 噪声相关性（目标 1）。相关性调制伴随所有增益变化的结果 这意味着相关性变化是大多数皮质计算中的主要因素。 我在独立阶段的目标是通过实验研究灵活性和适应性 相关性的变化可以影响皮质区域之间的交流。 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， 取决于神经元反应的组合方式。在我之前使用的任务中 实验中，噪声相关性限制了群体敏感性，注意力适应性下降 相关性（如归一化假设所预测）。在目标 2 中，我将使用一个任务，其中噪声 相关性可以提高而不是限制性能，以确定注意力是否可以自适应地 增加相关性。在目标 3 中，我将通过测量相关性来调查噪声相关性的起源 不同皮质区域之间的差异和不同的注意力来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 人口编码与感官信息从一个区域传输到另一个区域的方式的相关性 用于指导行为。