A dendritic nexus in the circuits that coordinate learning

协调学习的电路中的树突状连接

• 批准号: 10659554
• 负责人: Aaron Michael Kerlin
• 金额: $ 37.52万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659554
• 关键词: Action Potentials; Address; Anatomy; Apical; Area; Behavior; Behavior Control; Behavioral; Biological Models; Biological Neural Networks; Brain; Calcium; Calcium Spikes; Cells; Code; Cognition Disorders; Complex; Computer Models; Custom; Data; Dendrites; Elements; Event; Feedback; Future; Generations; Glutamates; Image; Individual; Intervention; Knowledge; Laboratories; Learning; Learning Skill; Machine Learning; Maps; Measures; Microscope; Modeling; Modification; Monitor; Motor; Motor Cortex; Mus; Nervous System; Neurons; Optics; Outcome; Output; Performance; Phase; Play; Population; Positioning Attribute; Process; Publishing; Recurrence; Role; Sensory; Short-Term Memory; Signal Transduction; Source; Synapses; Techniques; Testing; Thalamic structure; Weight; Work; behavior change; behavioral outcome; experience; experimental study; frontal lobe; improved; in vivo; inhibitory neuron; insight; machine learning algorithm; multi-photon; novel; optogenetics; response; segregation; sensory cortex; sensory feedback; signal processing; theories; transmission process; two-photon

Abstract When we learn a complex behavior the nervous system must continuously drive new actions, compare predictions for the actions against outcomes, and strengthen or weaken the connections between neurons (synapses) in order to improve future actions. However, within the multilayer brain networks that control behavior, the behavioral impact of modifying a synapse depends upon many downstream connections. Thus, learning requires the brain solve a ‘credit assignment’ problem: information about which synaptic modifications should be made is distributed across the network, yet must somehow be leveraged by local processes to guide change at individual synapses. A major gap in our ability to relate behavioral events to synaptic change is the current lack of knowledge of these local processes that guide synaptic changes at individual neurons. Recent theories of learning suggest that spikes generated in the apical dendrites of cortical neurons may play a key role in solving this credit assignment problem. The experiments in this proposal will test the hypothesis that the apical dendrites of neurons in the pre-motor cortex integrate multiple learning-instructive feedback sources, and – under appropriate conditions – generate dendritic spikes that rapidly reconfigure the connectivity and function of neurons. In these experiments we will use advanced optical techniques to monitor and manipulate activity in the dendrites of a subset of neurons in the frontal cortex that have a well-delineated role in action planning. A key prediction of our hypothesis is that the activity of the apical dendrites reflects local credit-related calculations and that this activity is distinct from the activity transmitted to other neurons by action potential generation near the cell body. We will test this using longitudinal two-photon calcium imaging of cortical neurons during learning to determine how the behavioral selectivity of dendrites and cell bodies change with changing behavior. In order to identify the contribution of dendritic spikes to learning, we will also use optogenetics to selectively suppress activity in the apical dendrites during learning. Computational models also predict that dendritic spikes are generated by a mismatch between outcome information arriving from long-range feedback projections and local inhibition that predicts this feedback. To test this, we will combine synaptic glutamate imaging and optogenetics to map the selectivity and anatomical identity of feedback projections to the apical dendrites, and calcium imaging to determine the selectivity of local inhibitory neurons that target the apical dendrites. Together, these studies will provide critical new insights into the circuit mechanisms governing cortical plasticity and credit assignment. In doing so, they will provide a key framework for connecting complex learning with modifications at the individual synapse level, and will build bridges between machine learning algorithms and models of biological neural networks.

抽象的 当我们学习一种复杂的行为时，神经系统必须不断驱动新的行为，比较 预测针对结果的行动，并加强或削弱神经元之间的连接 （突触）以改善未来的行动然而，在控制行为的多层大脑网络中， 修改突触的行为影响取决于许多下游连接，因此，学习。 要求大脑解决“信用分配”问题：关于应该进行哪些突触修饰的信息 made 分布在整个网络中，但必须以某种方式被本地流程利用来指导变革 我们将行为事件与突触变化联系起来的能力的一个主要差距是目前的缺乏。 指导单个神经元突触变化的这些局部过程的知识。 研究表明，皮质神经元顶端树突中产生的尖峰可能在解决问题中发挥关键作用 这个学分分配问题将检验顶端树突的假设。 前运动皮层中的神经元整合了多个学习指导反馈源，并且 - 在 适当的条件 - 产生树突尖峰，快速重新配置连接和功能 在这些实验中，我们将使用先进的光学技术来监测和操纵神经元的活动。 额叶皮层神经元子集的树突在行动计划中具有明确的作用。 我们的假设的预测是，顶端树突的活动反映了局部信用相关的计算和 这种活动与通过在神经元附近产生动作电位而传递给其他神经元的活动不同 我们将在学习过程中使用皮质神经元的纵向双光子钙成像来测试这一点。 确定树突和细胞体的行为选择性如何随着行为的变化而变化。 确定树突尖峰对学习的贡献，我们还将使用光遗传学来选择性抑制 学习过程中顶端树突的活动也预测树突尖峰。 由远程反馈预测和本地反馈预测得出的结果信息之间不匹配而产生 为了测试这一点，我们将结合突触谷氨酸成像和光遗传学。 将反馈投影的选择性和解剖学特性映射到顶端树突和钙成像 这些研究共同确定了针对顶端树突的局部抑制神经元的选择性。 将为控制皮质可塑性和信用分配的回路机制提供重要的新见解。 在此过程中，他们将提供一个关键框架，将复杂的学习与个人的修改联系起来。 突触级别，并将在机器学习算法和生物神经模型之间架起桥梁 网络。