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

点击翻译按钮获取中文摘要

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.