Active Inference in Hierarchical Brain Networks: Mechanisms, Functions, Modulation.

大脑的一项了不起的壮举是它能够将数十亿神经细胞的活动整合成一个统一的自我。然而，所涉及的机制本质上是未知的。此外，这种惊人的能力是脆弱的，当我们面对大脑和精神的痛苦时，我们仍然无能为力。因此，我的研究计划的首要目标是推进我们对大脑系统功能整合的机制理解。我的研究理念是脑回路的主动推理。通俗地说，我认为大脑是一台预测机器，它积极地预测输入，评估与实际外部事件的差异，并相应地更新内部上下文表示，以适应行为和学习。从机制上讲，我认为这种持续不断的循环流动是通过大脑网络中相互依赖的有节奏的波动来实现的。我的方法是多尺度电生理学（e-phys；从细胞到EEG/MEG全脑成像）。我还使用一系列脑刺激技术（光遗传学，经颅刺激，实时闭环感官刺激）来最好地捕捉和修改动物模型和人类志愿者大脑网络的快速动态。我也训练具有自然刺激序列的人工代理，以获得大脑网络中主动推理的代理，并产生神经生理活动的编码模型。我的探索研究计划结构如下：？目标1（模型和工具）将提供可测试的，振荡大脑网络的概念模型与受生物学启发的耦合振荡器，以澄清主动推理的机制。一个免费的开源软件套件，用于计算机密集型数据分析，可以建模和处理大量密集的神经生理数据；？ 目标2（功能）将提供大脑活动推理的经验证据，并将注意力集中在听觉工作记忆和自然输入的感知上；？ 目标3（调节）将提出新的基于证据的神经调节的新方法，包括生物学启发的大脑训练策略，以确定大脑功能和行为中多节奏集合波动的因果作用。我的项目建立在我们实验室在脑系统神经动力学研究方面的优势之上。它还将支持我的研究中新的基础和转化方向，与我建立的合作者网络。我将进一步致力于与工业合作伙伴将研究成果实际转移到可穿戴、低成本的设备上。总的来说，预期的结果将显著推进脑功能综合机制的基础知识。我预计，所揭示的原理和提供的工具将服务于研究界，以回答广泛的系统神经科学问题。

A remarkable feat of the brain is its capacity to integrate the activity of billions of nervous cells into a unified self. Yet, the nature of the mechanisms involved are essentially unknown. Further, this astonishing competence is vulnerable, and we remain clueless when confronted with the afflictions of the brain and the mind. The overarching goal of my research plan is therefore advance our mechanistic comprehension of functional integration in brain systems. My research concept is that of active inference by brain circuits. In lay terms, I see the brain as a predictive machine that actively forecasts its inputs, evaluates discrepancies with actual external events, and updates internal contextual representations accordingly for behavioral adaptation and learning. Mechanistically, I posit that this constant, recurrent flux is implemented by interdependent rhythmic fluctuations in brain networks. My methods are those of multiscale electrophysiology (e-phys; from cells to whole-brain imaging with EEG/MEG). I also use a range of brain stimulation techniques (optogenetics, transcranial stimulation, real-time closed-loop sensory stimulation) to best capture and causally modify the rapid dynamics of brain networks in animal models and human volunteers. I also train artificial agents with natural stimulus sequences to derive proxies of active inference in brain networks and produce encoding models of neurophysiological activity. My Discovery research plan is structured as follows: ? Aim 1 (Models & Tools) will deliver testable, conceptual models of oscillatory brain networks with coupled oscillators inspired by biology, to clarify the mechanisms of active inference. A free, open-source software suite for computer-intensive data analytics will enable the modeling and processing of large and dense neurophysiological data volumes; ? Aim 2 (Functions) will provide empirical evidence of brain active inference with a cohesive focus on auditory working memory and perception of naturalistic inputs; ? Aim 3 (Modulation) will propose new evidence-based approaches for targeted neuromodulation, including biologically-inspired brain training strategies, to establish the causal role of polyrhythmic ensemble fluctuations in brain functions and behavior. My program builds on my lab's strengths in the study of neural dynamics of brain systems. It will also support new fundamental and translational directions in my research, with my established network of collaborators. I will further aim at practical transfers of research outcomes to wearable, low-cost devices with an industrial partner. Overall, the expected outcomes will significantly advance basic knowledge of integrative mechanisms of brain functions. I anticipate the principles unveiled and tools deliver will serve the research community to answer a broad range of systems neuroscience questions.