The organization of neural representations for flexible behavior in the human brain

项目总结 认知控制使我们能够根据我们的目标灵活地指导我们的行动。最突出的理论的核心 认知控制的概念是控制表征。为使控制成功，将保持此表示形式 在工作记忆中，前额叶皮质(PFC)允许相同的输入映射到不同的反应 这取决于具体情况。一致的证据发现，PFC编码了多个任务相关的 任务的特点。然而，人们对这些控制表示的计算特性知之甚少 基于他们如何组织这些信息。这是我们认识上的一个根本差距。在这里，我们专注于 一个这样的性质，称为表象维度。在技术术语中，代表性 维度指的是解释神经群体活动差异所需的轴数 在它的输入上。理论神经科学已经证明，神经群体的维度 决定了一个基本的计算权衡。低维表示将丢弃不相关的 信息和形式对其输入的抽象。因此，它适合于推广到新的情况。一个 高维表示将输入的多个混合编码为高度可分离的激发模式 没有重叠。理解概括性和分离性与认知控制功能的关系 承诺在控制中的一些最基本的问题上取得进展，包括上下文引导行为， 干扰解决、多任务处理和受控到自动行为。 该研究计划的目标是将高维控制的计算特性 认知控制功能的表征。我们的总体假设是PFC形成高维 在受益于可分离性的行为环境中所需的任务特征的表示。 这一假说是由理论神经科学和基础研究推动的，这些研究已经测试了 动物模型中PFC表征的维度。然而，没有一项在人类中进行的研究 PFC中的维度编码和任何物种都没有证据将维度与认知控制功能联系起来。 通过NINDS R21(NS108380)，我们开发和提炼了两种新的、互补的方法 从fMRI和EEG数据估计表征维度。使用这些方法，我们发现 背外侧PFC(DLPFC)形成相对于其他脑的高维编码的初步证据 区域。我们还从脑电中发现证据表明，高维码的可分性提高了效率、灵活性 行为，并可能有助于稳定读出。因此，我们在这些初步观察的基础上建立了性质， 人脑中高维控制表征的功能意义和时间动力学。

PROJECT SUMMARY Cognitive control allows us to flexibly guide our actions based on our goals. Central to most prominent theories of cognitive control is the control representation. For control to be successful, this representation is maintained in working memory by the prefrontal cortex (PFC) where it allows the same input to map to different responses depending on the context. Convergent evidence has found that the PFC encodes multiple task-relevant features of a task. However, little is known about the computational features of these control representations based on how they organize this information. This is a fundamental gap in our understanding. Here we focus on one such property, termed representational dimensionality. In technical terms, representational dimensionality refers to the number of axes needed to explain the variance in activity of a neural population across its inputs. Theoretical neuroscience has demonstrated that the dimensionality of a neural population determines a fundamental computational trade-off. A low dimensional representation will discard irrelevant information and form abstractions over its inputs. It is therefore suitable for generalization to new situations. A high dimensional representation encodes multiple mixtures of inputs into highly separable firing patterns without overlap. Understanding how generalizability and separability relate to cognitive control function promises gains on some of the most fundamental problems in control, including context-guided behavior, interference resolution, multitasking, and controlled-to-automatic behavior. The goal of this research program is to link the computational properties of high dimensional control representations to cognitive control function. Our overall hypothesis is that PFC forms high dimensional representations of task features which are needed in behavioral circumstances benefitting from separability. This hypothesis is motivated by theoretical neuroscience and foundational studies that have tested the dimensionality of PFC representations in animal models. However, no study in humans has studied high dimensional codes in PFC and no evidence in any species links dimensionality to cognitive control function. Through an NINDS R21 (NS108380), we have developed and refined two novel, complementary methods for estimating representational dimensionality from fMRI and EEG data. Using these approaches, we have found preliminary evidence that the dorsolateral PFC (DLPFC) forms a high dimensional code relative to other brain areas. We also find evidence from EEG that separability of high dimensional codes improves efficient, flexible behavior and may aid stable readout. Thus, we build on these initial observations to establish the nature, functional significance, and temporal dynamics of high dimensional control representations in the human brain.