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

项目摘要 认知控制使我们能够根据目标灵活地指导我们的行动。最著名的理论的核心 认知控制的核心是控制表征。为了控制成功， 在工作记忆中，前额叶皮层（PFC）允许相同的输入映射到不同的反应 取决于上下文。收敛的证据发现，PFC编码多个任务相关的 一个任务的特点。然而，很少有人知道这些控制表示的计算功能 基于他们如何组织这些信息。这是我们认识上的一个根本性差距。在这里我们聚焦 一个这样的属性，称为表征维度。从技术上讲， 维度是指解释神经群体活动差异所需的轴数 在其输入。理论神经科学已经证明， 决定了一个基本的计算权衡。低维表示将丢弃不相关的 信息和表单抽象。因此，它适合于推广到新的情况。一 高维表示将输入的多个混合编码成高度可分离的激发模式 没有重叠。理解概括性和可分性如何与认知控制功能相关 承诺在控制中的一些最基本的问题上取得进展，包括上下文引导的行为， 干扰解决、多任务处理和控制到自动行为。 本研究计划的目标是将高维控制的计算特性 认知控制功能。我们的总体假设是，PFC形成高维 表示的任务特征，需要在行为的情况下受益于可分性。 这一假设的动机是理论神经科学和基础研究，已经测试了 动物模型中PFC表征的维度。然而，没有研究人类研究高 PFC中的维度代码，没有证据表明任何物种将维度与认知控制功能联系起来。 通过NINDS R21（NS 108380），我们开发并改进了两种新的互补方法， 从fMRI和EEG数据估计表征维度。使用这些方法，我们发现 初步证据表明，背外侧PFC（DLPFC）形成了一个相对于其他大脑的高维代码 地区我们还从EEG中发现证据，高维代码的可分离性提高了效率，灵活性， 行为，并可以帮助稳定的读出。因此，我们建立在这些初步观察，以建立性质， 功能的意义，和时间动态的高维控制表示在人脑中。

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.