Using Attention to Understand Cortical Population Codes

4.项目摘要 感知决策基于由神经元群体编码的感觉信息， 短时间。为了确定人口行为准则中最重要的方面，我改变了 视觉注意力，这改善了对所关注的位置或特征的感知。的信息量 编码在神经元群体中的基因的变异性受到单个皮层神经元的变异性和 在整个群体中共享的相关变异性（噪声相关性）。我发现注意力 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，忽略噪声相关性的单个神经元的研究可能错过了最重要的 神经元群体的反应指导行为的方式的关键方面，所以有必要 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时使用计算方法和多电极记录 灵长类动物了解神经元间的相关性出现的机制和程度， 能灵活适应任务要求。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的关系是联系在一起的：放电率变化最大的神经元 （通常增加乘法增益）显示出相关性的最大降低。我将用 该奖项的指导阶段，通过创建一个 计算模型来测试是否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）也可引起 噪声相关性（目标1）。相关性的调制伴随着所有增益变化的结果 这意味着相关性变化是大多数皮层计算的主要因素。 我在独立阶段的目标是实验性地研究如何灵活和适应 相关性的变化可能是大脑皮层区域之间的交流的相关性的影响。 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， 这取决于神经元反应的组合方式。在我以前的任务中， 实验中，噪声相关性限制了群体的敏感性，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，我将使用一个任务， 相关性提高，而不是限制，性能，以确定是否注意力可以自适应 增加相关性。在目标3中，我将通过测量相关性来研究噪声相关性的起源 不同的皮质区域和不同的注意力之间的关系，以确定相关性是否取决于 共享功能输入的强度。总的来说，这些研究将对以下方面的影响产生影响： 人口编码和感官信息从一个地区传递到另一个地区的方式之间的相关性， 用于指导行为。

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.