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

分层大脑网络中的主动推理：机制、功能、调制。

• 批准号: RGPIN-2020-06889
• 负责人: Baillet, Sylvain
• 金额: $ 5.68万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=761335
• 关键词: Active; Inference; Hierarchical; Brain; Networks

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.

大脑的一项非凡成就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，所涉及的机制的性质基本上是未知的。此外，这种惊人的能力是脆弱的，当我们面对大脑和心灵的痛苦时，我们仍然一无所知。 因此，我的研究计划的总体目标是推进我们对大脑系统功能整合的机械理解。 我的研究概念是大脑回路的主动推理。通俗地说，我认为大脑是一台预测机器，它积极预测其输入，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，我认为这种持续的、周期性的流动是由大脑网络中相互依赖的节奏波动实现的。 我的方法是多尺度电生理学（e-phys;从细胞到EEG/MEG的全脑成像）。我还使用了一系列脑刺激技术（光遗传学，经颅刺激，实时闭环感觉刺激），以最好地捕捉和因果地修改动物模型和人类志愿者大脑网络的快速动态。我还用自然刺激序列训练人工代理，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（模型与工具）将提供可测试的，概念性的振荡脑网络模型与耦合振荡器的灵感来自生物学，以澄清主动推理的机制。用于计算机密集型数据分析的免费开源软件套件将能够建模和处理大量密集的神经生理数据;目标2（功能）将提供经验证据的大脑主动推理与凝聚力的重点放在听觉工作记忆和感知的自然输入;？目标3（调制）将提出新的基于证据的靶向神经调制方法，包括生物启发的大脑训练策略，以建立多节奏整体波动在大脑功能和行为中的因果作用。我的计划建立在我的实验室的优势，在研究大脑系统的神经动力学。它还将支持我的研究中新的基础和翻译方向，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的低成本设备上。总的来说，预期的结果将显着推进大脑功能的整合机制的基础知识。我预计，所揭示的原则和工具将有助于研究界回答广泛的系统神经科学问题。