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 当我们了解复杂的行为时，神经系统必须继续推动新的动作，请进行比较 预测针对结果的行动，并加强或削弱神经元之间的联系 （突触）为了改善未来的行动。但是，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。那，学习 需要大脑解决“信用分配”问题：有关哪种突触修改的信息 制造是在整个网络上分布的，但必须通过本地流程以某种方式指导更改 个体突触。我们将行为事件与突触变化联系起来的主要差距是当前缺乏 了解指导单个神经元合成变化的这些局部过程。最近的理论 学习表明，皮质神经元的顶端树突中产生的尖峰可能在解决方面起关键作用 此信用分配问题。该提案中的实验将检验以下假设：顶端树突 运动前皮层中的神经元集成了多个学习教学反馈来源，并且 - 适当的条件 - 生成树突尖峰，以快速重新配置 神经元。在这些实验中，我们将使用先进的光学技术来监视和操纵活动 额叶皮层中神经元子集的树突在动作计划中具有很好的作用。钥匙 我们假设的预测是，顶端树突的活动反映了与当地信用相关的计算和 该活动与通过附近的动作电位产生传递给其他神经元的活性不同 细胞体。我们将使用在学习期间对皮质神经元的纵向两光子钙成像进行测试 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。为了 确定树突状峰值对学习的贡献，我们还将使用光遗传学有选择地抑制 学习过程中顶端树突的活动。计算模型还预测树突状尖峰是 通过从远程反馈项目到达的结果信息与本地的结果信息之间的不匹配产生 预测这种反馈的抑制作用。为了测试这一点，我们将结合突触谷氨酸成像和光遗传学 将反馈项目的选择性和解剖学身份映射到顶端树突和钙成像 确定针对顶端树突的局部抑制神经元的选择性。在一起，这些研究 将为有关皮质可塑性和信用分配的电路机制提供关键的新见解。 这样一来，他们将提供一个关键框架，以将复杂的学习与个人的修改联系起来 突触水平，将在机器学习算法和生物神经模型之间建造桥梁 网络。