Using Attention to Understand Cortical Population Codes

描述（由申请人提供）：感知决策基于神经元群体在短时间内编码的感觉信息。为了确定指导行为的人口代码的最重要的方面，我改变了视觉注意力，这提高了对关注位置或特征的感知。在神经元群体中编码的信息量受到单个皮层神经元的可变性和在群体中共享的相关可变性（噪声相关性）的限制。我发现，共享变异性的注意力变化可能是导致行为改善的主要因素。这些结果表明，忽略噪声相关性的单个神经元研究可能错过了神经元群体反应指导行为的最关键方面，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。 这个提议的目标是使用计算方法和清醒灵长类动物的多电极记录来了解神经元间相关性产生的机制以及它们能够灵活适应任务需求的程度。在我之前的实验中，我发现注意力对放电率的调节和噪声相关性是有联系的：放电率变化最大的神经元（通常是倍增增益增加的神经元）的相关性下降最大。我将利用这个奖项的指导阶段，通过创建一个计算模型来研究噪声相关性的起源和灵活性，以测试被认为是许多感觉，运动和认知过程（感觉正常化）中单个神经元增益变化的基础的机制是否也可能导致噪声相关性的调制（目标1）。相关性的调制伴随着所有增益的变化，这一结果意味着相关性的变化是大多数皮层计算中的一个主要因素。 我在独立阶段的目标是通过实验研究相关性变化的灵活性和适应性，以及相关性对皮层区域之间通信的影响。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，这取决于神经元响应的组合方式。在我之前实验中使用的任务中，噪声相关性限制了群体敏感性，注意力自适应地降低了相关性（正如归一化假设所预测的那样）。在目标2中，我将使用一个任务，其中噪声相关性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，我将通过测量不同皮质区域和不同注意力之间的相关性来研究噪音相关性的起源，以确定相关性是否取决于共享功能输入的强度。总的来说，这些研究将对相关性对群体编码的影响以及感官信息从一个区域传递到另一个区域并用于指导行为的方式产生影响。 公共卫生相关性：许多神经系统疾病，包括抑郁症，精神分裂症和注意缺陷多动障碍（ADHD）被认为涉及皮层神经元网络，因此了解神经元群体编码和读取信息的方式对于诊断和开发治疗这些疾病的药物疗法至关重要。本提案中的项目将阐明群体编码的一般机制，特别是将增强我们对注意力背后的神经元机制的理解。注意力缺陷，如ADHD，被认为影响了美国多达5%的儿童，需要更好地了解与注意力相关的基本神经机制，以指导注意力缺陷的评估，诊断和治疗。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: Many neurological diseases including depression, schizophrenia, and attention deficit hyperactivity disorder (ADHD) are thought to involve networks of cortical neurons, so understanding the way that information is encoded and read out by neuronal populations will be critical for diagnosing and developing drug therapies to treat these diseases. The projects in this proposal will elucidate general mechanisms for population coding, and will in particular enhance our understanding of the neuronal mechanisms underlying attention. Deficits of attention such as ADHD are thought to affect as many as 5% of the children in the United States, and better understanding of the basic neuronal mechanisms related to attention is needed for guiding assessment, diagnosis, and treatment of attentional deficits.