Layer-specific cortical feedback dynamics - human ultra-high resolution functional brain imaging for predictive brain functions

开车去上班时，你会本能地处理周围的事情，同时想象你的晨会。我的目标是了解大脑的视觉部分如何对智力的这一特征做出贡献：将感知的经验与世界的内部心理模型联系起来，并做出未来预测。人类大脑的这种认知能力被认为是在其时间范围和灵活性上将我们与动物区分开来的心理能力之一。我认为，人脑使用不同的信息处理流来感知当前时刻，并在更长的持续时间内对目标导向行为的未来行动的结果进行心理模拟。因此，我们进化出了神经元计算，它将精确的时空感觉输入与上下文联系在一起，但也在精神上与在线感知分离，以评估更长时间范围内的感觉状态。“预测性大脑”理论框架为理解这些过程提供了一种方案。该框架的中心原则是大脑进行推理和预测，以处理其高度结构化和动态的环境，推理不同时间尺度上的规律，形成抽象预测，评估随着时间推移展开的未来环境。预测处理框架通过提供指导经验性大脑研究的总体理论框架，改变了21世纪的神经科学。该框架在实验上是容易处理的，我们可以用先进的神经科学方法对其进行测试，以将该框架的假设原则与跨越多个大脑组织和功能的数据进行同化。预测处理帐户通过训练神经元生成解释世界的内部模型来描述大脑如何在其环境中学习。由于神经元网络通过感觉通路间接进入世界，内部模型通过形成对感觉事件的预测并将其与实际感觉信号进行比较来优化。残留的或令人惊讶的信息(预测误差)是在分层的大脑皮层水平上计算和处理的，在那里它被用来修改心理模型。我开发了刺激范式和功能磁共振成像方法，以确定预测是如何在人类大脑皮层微回路中组织起来的。事实证明，这些脑成像数据对于限制计算模型、生物启发的人工智能以及灵长类和啮齿动物的侵入性神经元记录至关重要。我的提案概述了一个新的假设，将在中尺度脑成像中进行测试。当感觉信息通过皮质区域被处理时，较高的区域会连续地将感觉预测反馈给较低的区域，这些区域源自我们先前的预期。因此，预测性的大脑信号必须预见到感觉处理的丰富时空结构。例如，早期的视觉系统接收不断流动的感觉信号，因此预测反馈处理的时间必须与前馈神经活动的时间动力学相兼容，以便大脑随着时间的推移更新对我们感知的预测。然而，大脑不仅需要在当前环境中行动，还需要计划未来的行为。我认为，大脑皮层预测反馈具有行为相关的时间结构，用于表征当前时刻或未来表征。使用开创性的超高场高分辨率人类功能脑成像，我将研究早期视觉皮质微电路中预测性反馈的时间代码。我将应用范式来测试感知(即涉及毫秒范围内的反馈，如运动错觉)、认知(即需要几秒钟内的反馈，如规划路线导航)和心理意象(即设想当前不存在的对象)的时间预测。

Driving to work, you instinctively process your surroundings whilst at the same time imagining your morning meeting. My ambition is to understand how the visual parts of the brain contribute to this feature of intelligence: associating perceived experience with internal mental models of the world, and making future predictions. This cognitive capacity of the human brain has been suggested as one of the mental abilities that in its temporal extent and flexibility separates us from animals. I propose human brains use distinct information processing streams for perceiving the present moment and for mentally simulating consequences of future actions for goal-directed behaviour over longer durations. As such, we have evolved neuronal computations that contextualise precise spatiotemporal sensory inputs, but also mentally decouple from online perception to evaluate sensory states over longer time frames. The 'Predictive Brain' theoretical framework offers a scheme to understand these processes. The framework's central tenet is that the brain performs inference and prediction to process its highly structured and dynamic environment, inferring regularities over different temporal scales to form abstract predictions evaluating future surroundings unfolding over time.The 'Predictive Processing' framework has transformed neuroscience in the 21st century by offering an overarching theoretical framework guiding empirical brain research. The framework is experimentally tractable, and we can test it with advanced neuroscientific methods to assimilate the framework's hypothetical principles with data spanning multiple levels of brain organisation and function. Predictive processing accounts describe how brains learn in their environments by training neurons to generate internal models that explain the world. Since neuronal networks indirectly access the world via sensory pathways, internal models are optimised by forming predictions of sensory events and comparing them with actual sensory signals. The residual, or surprising, information (prediction error) is computed and processed upwards in hierarchical cortical levels where it is used to revise mental models. I have developed stimulation paradigms and fMRI approaches towards establishing how predictions are organised in human cortical microcircuits. Such brain imaging data are proving essential to constrain computational models, biologically inspired artificial intelligence and invasive neuronal recordings in primates and rodents.My proposal outlines a novel hypothesis to be tested in mesoscale brain imaging. When sensory information is processed through cortical areas, higher areas successively feed back sensory predictions to lower areas derived from our prior expectations. Hence predictive brain signals must anticipate the rich spatial and temporal structure of sensory processing. For example, the early visual system receives a constant flow of sensory signals, and the timing of predictive feedback processing must therefore be compatible with the temporal dynamics of feedforward neural activity in order for the brain to update predictions of our perceptions over time. However, brains not only need to act in their current environment but need to plan future behaviours. I propose that cortical predictive feedback has a behaviourally relevant temporal structure, for representing the present moment or future representations. Using pioneering, ultra high-field, high-resolution human functional brain imaging, I will investigate temporal codes of predictive feedback in early visual cortical microcircuits. I will apply paradigms testing temporal predictions for perception (i.e. involving feedback in the millisecond range, such as motion illusions), for cognition (i.e. requiring feedback over seconds such as planning a route navigation), and for mental imagery (i.e. envisaging an object not currently present).