A Novel Approach to Crack Neuronal Mechanisms that Shape Computations in the Brain

破解塑造大脑计算的神经元机制的新方法

• 批准号: 10472225
• 负责人: Hidehiko Inagaki
• 金额: $ 173.7万
• 依托单位: MAX PLANCK FLORIDA CORPORATION
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-19 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10472225
• 关键词: Algorithms; Amaze; Area; Artificial Intelligence; Attention deficit hyperactivity disorder; Behavior; Behavioral Paradigm; Brain; Brain Diseases; Electrophysiology (science); Environment; Foundations; Functional disorder; Goals; Learning; Link; Machine Learning; Mediating; Methods; Molecular; Neurobiology; Neurons; Neurosciences; Obsessive-Compulsive Disorder; Pattern; Process; Shapes; Symptoms; Synapses; Synaptic plasticity; System; adaptive learning; flexibility; improved; maladaptive behavior; novel; novel strategies; relating to nervous system; screening

Project Summary Living in this ever-changing environment, we continually adapt and learn new behaviors. The computation mechanisms in our brain must be highly plastic to support such flexibility. Problems with this adaptive process, on the other hand, result in inflexible and maladaptive behaviors, the main symptoms of Attention-Deficit / Hyperactivity Disorder (ADHD), Obsessive-Compulsive Disorder (OCD), and other brain disorders. The goal of this proposal is to establish an experimental method to approach what neuronal substrates and mechanisms support our flexible behavior. Neuronal computations are mediated by coordinated activity patterns across neurons, referred to as neuronal dynamics. When we learn/adapt behavior, underlying neuronal dynamics change. This reconfiguration of neuronal dynamics is constrained by synaptic interactions among neurons. Thus, changes in synaptic interactions, or synaptic plasticity, likely mediate the shaping of neuronal dynamics and behavior. Therefore, neuronal dynamics and synaptic plasticity are two pillars of brain functions that cooperate to flexibly adapt behaviors. Yet, dynamics and plasticity have rarely been studied together due to the lack of appropriate behavioral paradigms and experimental methods to examine them. To bridge this gap, I have established a novel behavioral paradigm along with a proposal for a molecular screening method to identify the brain areas where synaptic plasticity is responsible for adapting actions. Electrophysiological recordings at such brain areas combined with manipulation of plasticity will allow us to crack a causal relationship between plasticity and neuronal dynamics: we will identify what kinds of changes in neuronal dynamics are induced by synaptic plasticity and how those changes result in improved behavior. By combining molecular, systems, and theoretical neuroscience methods, our experimental approach will link plasticity, dynamics, and behavior to explain the algorithmic basis of flexible computations in the brain. Identifying neural substrates controlling flexible behaviors provides a foundation for examining how dysfunctions of such substrates result in maladaptive behaviors observed in various brain disorders. Our novel approach to combine electrophysiology with manipulations of plasticity has the potential to become a new standard in neuroscience; and, linking neuronal dynamics and plasticity may inspire novel methods in machine learning and artificial intelligence. Thus, our findings and unconventional approach will have a broad impact in neuroscience and beyond.

项目摘要 我们生活在这种不断变化的环境中，我们不断适应并学习新的行为。计算 我们大脑中的机制必须高度塑性，以支持这种灵活性。这个自适应过程的问题， 另一方面，导致不灵活和适应不良的行为，注意力缺陷的主要症状 / 多动障碍（ADHD），强迫症（OCD）和其他脑部疾病。目标 该建议是建立一种实验方法，以接近神经元底物和机制 支持我们的灵活行为。 神经元计算是由神经元之间的协调活性模式介导的，称为神经元 动力学。当我们学习/适应行为时，基本的神经元动态会发生变化。这种重新配置 神经元动力学受神经元之间的突触相互作用的约束。因此，突触的变化 相互作用或突触可塑性可能介导神经元动力学和行为的塑造。所以， 神经元动力学和突触可塑性是脑功能的两个支柱，可灵活地适应 行为。然而，由于缺乏合适的 行为范式和实验方法检查它们。为了弥合这一差距，我已经建立了一本小说 行为范式以及针对分子筛选方法的提案，以识别大脑区域 突触可塑性负责适应动作。这些大脑区域的电生理记录 结合对可塑性的操纵将使我们能够破解可塑性和 神经元动力学：我们将确定神经元动力学的哪种变化是由突触可塑性诱导的 以及这些变化如何改善行为。通过结合分子，系统和理论 神经科学方法，我们的实验方法将连接可塑性，动力学和行为，以解释 大脑灵活计算的算法基础。 识别控制柔性行为的神经底物为检查功能障碍提供了基础 这种底物导致各种脑疾病中观察到的不良适应行为。我们的新方法 将电生理学与可塑性操纵相结合有可能成为新标准 神经科学；并且，将神经元动力和可塑性联系起来可能会激发机器学习和 人工智能。因此，我们的发现和非常规的方法将对神经科学产生广泛的影响 及以后。