Developing Quantitative Methods to Reveal Neural Circuit Dynamics

开发定量方法来揭示神经回路动力学

• 批准号: 10582556
• 负责人: Javier Josue How
• 金额: $ 7.85万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10582556
• 关键词: Algorithms; Anatomy; Animals; Automobile Driving; Award; Behavior; Behavioral; Brain; Brain Diseases; Caenorhabditis elegans; Chemicals; Code; Communication; Diacetyl; Ensure; Future; Goals; Grain; Graph; Head; Health; Individual; Information Theory; Interneurons; Label; Link; Methods; Molecular; Motor; Movement; Names; Negative Valence; Nematoda; Nervous System; Nervous System Physiology; Neurons; Neurosciences; Odors; Organism; Pattern; Phase; Population; Positive Valence; Postdoctoral Fellow; Preparation; Process; Property; Publications; Research; Research Project Grants; Rest; Signal Transduction; Stereotyping; Stimulus; System; Techniques; Testing; Time; Transgenic Organisms; Work; behavioral response; cost; design; experience; graph theory; information model; information processing; insight; interest; neural; neural circuit; neural patterning; novel; optogenetics; sensory feedback; sensory stimulus; theories; tool; transmission process

Project Summary/Abstract Patterns of neural activity underlie information processing in the brain. Most work to date has focused on separate stages of computation by looking at separate regions in the brain - one at a time. We propose that techniques from graph theory can help us better understand how information is processed by entire populations of neurons. To this end, we use the nematode Caenorhabditis elegans to study the processing of an ecologically-relevant signal in most of the nervous system at once. Specifically, we will record activity from all of the neurons in the head of the worm, where most olfactory processing occurs, while we expose the animal to an innately attractive odor, diacetyl. We will then test how this representation changes in two behavioral states – after adaptation, at which point the worm no longer finds diacetyl attractive, and when C. elegans recovers from adaptation, when it again finds diacetyl attractive. We will do all of this in a transgenic worm which will allow us to identify all neurons by name, and thus to analyze our results based on the known anatomical connections between neurons. Work we are preparing to submit for publication has established that one graph-theoretic feature can identify a stimulus’ valence, i.e. whether or not it is attractive or repellent, and we will determine which neurons are driving changes in this feature. Finally, we will optogenetically test the predictions from our analyses to ensure that they are biologically significant. For instance, some non-overlapping subsets of neurons may represent positive and negative valence, and their activation may induce either forward, or backward, movements, respectively. If, for example, a neuron provides an important link between neurons that represent any valence (i.e. it is on the shortest path between these neurons) and the motor command interneurons, then we might reason that it facilitates the transfer of information, and that inhibiting it would delay the animal’s odor-seeking behavior. A future extension of this work would combine graph theory and information theory to understand how efficiently neurons process and transfer information. Importantly, this field, called network coding, proposes that an efficient way to transmit information is to allow downstream nodes to decode information that is processed along the path. During my postdoctoral work, as I gain experience with new theories and a new neural system that uses spiking neurons, I seek to develop the field of network coding for neuroscience. I am interested in spiking neurons to ensure my work is applicable to the larger field of neural systems which employ spiking, not graded, potentials.

项目摘要/摘要 神经活动的模式是大脑中信息处理的基础。迄今为止的大多数工作都集中在 在计算的单独阶段，通过查看大脑中的单独区域 - 一次。我们 提议，图理论的技术可以帮助我们更好地理解信息的处理方式 由整个神经元种群。为此，我们使用线虫秀丽隐杆线虫研究 在大多数神经系统中，生态相关信号的处理。具体来说， 我们将记录来自蠕虫头部所有神经元的活动，其中大多数嗅觉处理 发生，当我们将动物暴露于天生有吸引力的气味时，二乙酰基。然后，我们将测试如何 在适应后的两个行为状态中的表示变化，此时蠕虫不再 发现二乙酰基有吸引力，当秀丽隐杆线虫从适应中恢复时，它再次发现二乙酰基 吸引人的。我们将在转基因蠕虫中完成所有这些，这将使我们能够按名称识别所有神经元， 因此，基于神经元之间已知的解剖联系来分析我们的结果。工作 我们正准备提交出版，确定一个图理论功能可以识别 刺激的价，即它是否有吸引力或驱虫剂，我们将确定哪些神经元 正在推动此功能的变化。最后，我们将对分析的预测进行吻合测试 确保它们具有生物学意义。例如，一些神经元的非重叠子集 可能代表正价和负面价，它们的激活可能会诱导前进，或者 向后，运动。例如，神经元提供了重要的联系 代表任何价值的神经元（即它在这些神经元之间的最短路径上）和电动机 命令中间神经元，然后我们可能会认为它有助于信息的传递，并且 抑制它会延迟动物寻求气味的行为。这项工作的未来扩展将 结合图理论和信息理论，以了解如何有效地神经元过程和 转移信息。重要的是，该领域称为网络编码，提案是一种有效的方法 传输信息是为了允许下游节点解码沿着处理的信息 小路。在博士后工作中，随着我获得新理论和新神经系统的经验 使用尖峰神经元，我寻求开发用于神经科学的网络编码领域。我是 对尖峰神经元感兴趣，以确保我的工作适用于更大的神经元系统领域 员工尖峰（未分级）潜力。