Integrating and separating information sequences in the human cerebral cortex

项目摘要 认知的许多方面都是随着时间而延伸的。听到声音的片段，我们把它当作一个声音的一部分。 模仿鸟的旋律;听到一个词，我们把它理解为一个有意义的句子的一部分。所以我们的 大脑必须具备整合信息的能力。然而，信息不能被整合 不加区别地：一个新句子的主语不一定与前一个句子的动词有关， 我们可能想把这些信息分开这项工作的目标是了解 我们的大脑灵活地整合相关信息，同时分离不相关信息的算法。在 此外，我们的目标是了解时间整合和分离是如何在皮层电路中实现的， 我们如何操纵这些大脑过程。我们先前的研究表明，大脑在执行这项任务时， 分布式方式：人类大脑皮层的几乎所有区域都可以随着时间的推移整合信息。早期感觉 区域在短周期（毫秒到秒）上积分并且它们将信息传递给更高阶的区域， 其在较长的时间段（秒到分钟）上积分。我们为此过程提出以下算法： 每个皮层区域保持其自己的局部记忆，并试图形成一个合成的联合表示， 它的本地存储器和它接收的任何输入。当这个合成成功时，一个皮层区域将通过 将其合成的表示转发到下一个处理阶段。但如果合成不成功， 其本地存储器将被重置。例如，早期皮层区域可以合成单词中的音节， 然后在新单词的开头重置上下文。为了测试大脑是否在使用这种算法，我们将 模拟了人们在听时整合和分离信息序列的大脑中的功能磁共振成像活动 到复杂的故事要理解时态集成和时态分离是如何在活动中实现的， 我们还将测量ECoG信号，它提供了一个直接读出的同步和 皮层神经元的异步活动。我们假设，当大脑皮层的神经回路 它们不能将新的输入与先前的上下文相结合。我们将测试这种低频下降是否 同步化使得从外界进入皮层层次的信息流增加。最后我们将 开发工具来控制时间整合或分离的状态，使用电调制来增加或 降低皮层回路的同步性总之，这项工作提供了检测和操纵 在人类大脑的时间整合和分离过程，以及一个计算框架， 我们如何解析信息序列。我们的方法是创新的，因为我们开发了多尺度 实验范例和联合收割机数据跨功能磁共振成像，皮层脑电图，和建模。我们希望这项工作能帮助揭示 人类皮层回路整合和分离信息序列的层次机制。

Project Summary Many aspects of cognition extend over time. Hearing a fragment of sound, we perceive it as part of a mockingbird’s melody; hearing one word, we understand it as part of a meaningful sentence. Therefore, our brains must possess the ability to integrate information over time. However, information cannot be integrated indiscriminately: the subject of a new sentence is not necessarily related to the verb of the previous sentence, and we may want to keep these items of information separate. The goal of this work is to understand the algorithms by which our brains flexibly integrate related information while separating unrelated information. In addition, we aim to understand how temporal integration and separation are implemented in cortical circuits, and how we can manipulate these brain processes. Our prior work suggests that brains perform this task in a distributed manner: almost all regions of the human cortex can integrate information over time. Early sensory regions integrate over short periods (milliseconds to seconds) and they pass information to higher-order regions, which integrate over longer periods (seconds to minutes). We propose the following algorithm for this process: each cortical region maintains its own local memory, and attempts to form a synthesized joint representation of its local memory and any input that it receives. When this synthesis is successful, a cortical region will pass forward its synthesized representation to the next stage of processing. But if the synthesis is unsuccessful, then its local memory will be reset. For example, an early cortical region may synthesize syllables within a word but then reset its context at the beginning of a new word. To test whether the brain is using this algorithm, we will model fMRI activity in the brains of people who integrate and separate sequences of information as they listen to complex narratives. To understand how temporal integration and separation are implemented in the activity of cortical circuits, we will also measure ECoG signals, which provide a direct read-out of synchronous and asynchronous activity in cortical neurons. We hypothesize that cortical circuits become less synchronized when they cannot integrate new input with prior context. We will test whether this decrease in low-frequency synchronization allows increased information flow from the world into the cortical hierarchy. Finally, we will develop tools to control the state of temporal integration or separation, using electrical modulation to increase or decrease synchronization in cortical circuits. Altogether, this work provides tools to detect and manipulate temporal integration and separation processes in the human brain, as well as a computational framework for how we parse sequences of information. Our approach is innovative because we develop multi-scale experimental paradigms and combine data across fMRI, ECoG, and modeling. We expect this work to help reveal the hierarchical mechanisms by which human cortical circuits integrate and separate sequences of information.