The dynamics of neuronal population signalling during the temporal flow of perceptual events.

感知事件时间流期间神经元群信号传导的动态。

• 批准号: 406679869
• 负责人: Professorin Dr. Kristine Krug
• 金额: --
• 依托单位: Institut für Biologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/406679869?language=en
• 关键词: dynamics; neuronal; population; signalling; during

When we walk along a busy street against the flow of people, looking for someone we hope to meet, we face a flood of visual inputs. In this situation, the brain mechanisms underlying visual processing are engaged continuously and for an unpredictable length of time. They must analyse incoming sensory information continuously to evaluate, initiate and guide motor actions at all times (walking, avoiding obstacles, scanning faces, etc). In contrast, most of our knowledge of the neuronal basis of visual processing is based on simple ‘laboratory’ situations: discrete trials with predictable start (cue), a fixed stimulus, end (another cue) and motor action (one of a few known alternative responses). One of the next major challenges for systems neuroscience will be to incorporate in our experimental paradigms some aspects of ‘normal vision’ such as the continuous integration of information over time and the ongoing evaluation for motor actions. My current proposal builds onto the well-defined experimental framework of perceptual decision-making, but rather than treating perception and behaviour as a sequence of discrete, finite episodes, each culminating in a decision, new experimental paradigms will probe how the brain engages in active, continuous monitoring of the dynamically changing flow of information. Previous work by myself and others has shown that neurons in extrastriate visual area V5/MT of primates can control 3D and motion components of a complex perceptual experience. Undertaking high-dimensional recordings from many neurons simultaneously in this well-described area of the visual system of awake behaving primates, I propose to investigate the broader questions of how neurons interact dynamically in space and time in order to shape visual perception and decision-making. This project has four parts. Firstly, in order to probe the role of cooperativity in neuronal circuits for visual perception, I will introduce unpredictable dynamic changes in visual stimuli and investigate the temporal relationship between these stimulus changes and percept-related neuronal activity and interactions. Do dynamical responses provide evidence for hysteresis in state-dependent neuronal interactions? Secondly, as a visual 3D-motion percept emerges, we will track the interactions between task-relevant neurons across functional subdomains like columns in real time. As a bistable stimulus is viewed over time (seconds), we will investigate the relationship between changes in neuronal interactions and the reported percept. Thirdly, we will test whether neuronal response patterns obtained with simple motion and 3D stimuli predict responses to more complex visual stimuli (such as biological motion and 3D motion patterns embedded in movie sequences). Lastly, we will employ the empirical data obtained from these high-dimensional recordings to challenge neuro-computational models of network dynamics for perceptual decisions and collaborate on their construction.

当我们逆着人流走在繁忙的街道上，寻找我们希望见到的人时，我们面临着大量的视觉输入。在这种情况下，视觉处理背后的大脑机制会持续运转，并且持续的时间长度是不可预测的。他们必须持续分析传入的感官信息，以随时评估、启动和指导运动动作（行走、避开障碍物、扫描面部等）。相比之下，我们对视觉处理神经元基础的大部分了解都基于简单的“实验室”情况：具有可预测的开始（提示）、固定刺激、结束（另一个提示）和运动动作（少数已知的替代反应之一）的离散试验。系统神经科学的下一个主要挑战之一将是将“正常视觉”的某些方面纳入我们的实验范式，例如随着时间的推移信息的持续整合以及对运动动作的持续评估。我目前的提议建立在明确的感知决策实验框架的基础上，但新的实验范式将探讨大脑如何主动、连续地监控​​动态变化的信息流，而不是将感知和行为视为一系列离散的、有限的事件，每个事件最终都会做出一个决定。 我和其他人之前的工作表明，灵长类动物纹外视觉区域 V5/MT 中的神经元可以控制复杂感知体验的 3D 和运动成分。在清醒行为灵长类动物视觉系统的这个被充分描述的区域中，同时对许多神经元进行高维记录，我建议研究更广泛的问题，即神经元如何在空间和时间上动态相互作用，以塑造视觉感知和决策。该项目有四个部分。首先，为了探讨协同性在视觉感知神经元回路中的作用，我将引入视觉刺激中不可预测的动态变化，并研究这些刺激变化与感知相关的神经元活动和相互作用之间的时间关系。动态反应是否为状态依赖的神经元相互作用中的滞后现象提供了证据？其次，随着视觉 3D 运动感知的出现，我们将实时跟踪跨功能子域（如柱）的任务相关神经元之间的相互作用。随着时间（秒）观察双稳态刺激，我们将研究神经元相互作用的变化与报告的感知之间的关系。第三，我们将测试通过简单运动和 3D 刺激获得的神经元反应模式是否可以预测对更复杂的视觉刺激（例如嵌入电影序列中的生物运动和 3D 运动模式）的反应。最后，我们将利用从这些高维记录中获得的经验数据来挑战网络动力学的神经计算模型以进行感知决策并协作构建它们。