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: 9916237
• 负责人: Javier Josue How
• 金额: $ 3.89万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9916237
• 关键词: Algorithms; Anatomy; Animals; Automobile Driving; Award; Behavior; Behavioral; Biological; Brain; Brain Diseases; Caenorhabditis elegans; Chemicals; Code; Communication; Diacetyl; Ensure; Equilibrium; Future; Goals; Grain; Graph; Head; Health; Individual; Information Theory; Interneurons; Label; Link; Methods; Molecular; Motor; Movement; Names; Negative Valence; Nematoda; Nervous System Physiology; Nervous system structure; Neurons; Neurosciences; Odors; Organism; Pattern; Phase; Population; Positive Valence; Preparation; Process; Property; Publications; Research; Research Project Grants; Rest; Signal Transduction; Stereotyping; Stimulus; Structure; System; Techniques; Testing; Time; Transgenic Organisms; Work; base; behavioral response; cost; design; experience; graph theory; information model; information processing; insight; interest; neural circuit; neural patterning; novel; optogenetics; relating to nervous system; 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信息处理的基础。到目前为止，大多数工作都集中在 通过观察大脑中的不同区域--一次一个，对计算的不同阶段进行研究。我们 提出图论中的技术可以帮助我们更好地理解信息是如何处理的 通过整个神经元群体。为此，我们利用线虫秀丽线虫进行研究 在大多数神经系统中同时处理与生态相关的信号。具体来说， 我们将记录蠕虫头部所有神经元的活动，在那里大多数嗅觉处理 当我们将动物暴露在天生诱人的气味双乙酰中时，就会发生这种情况。然后我们将测试这一点 在适应之后，蠕虫不再在两种行为状态下的表示变化 发现双乙酰有吸引力，当线虫从适应中恢复时，当它再次发现双乙酰 很有吸引力。我们将在转基因蠕虫中完成所有这一切，这将使我们能够通过名称识别所有神经元， 从而根据神经元之间已知的解剖学联系来分析我们的结果。工作 我们正在准备提交出版已经确定了一个图论特征可以识别 刺激的价态，即它是吸引的还是排斥的，我们将确定哪些神经元 正在推动这一功能的变化。最后，我们将对我们的分析中的预测进行光遗传学测试 以确保它们具有生物学意义。例如，一些不重叠的神经元子集 可以代表正价和负价，它们的激活可以诱导正向或负价 向后，分别是运动。例如，如果一个神经元提供了一个重要的联系 代表任何价态的神经元(即它位于这些神经元之间的最短路径上)和运动 命令中间神经元，那么我们可能会推断它促进了信息的传递，并且 抑制它会延缓动物寻找气味的行为。这项工作的未来扩展将是 结合图论和信息论来理解神经元处理和 传输信息。重要的是，这个称为网络编码的领域提出了一种有效的方法来 传输信息的目的是允许下游节点解码沿 路径。在我的博士后工作中，随着我对新理论和新神经系统的经验积累 使用尖峰神经元，我试图为神经科学发展网络编码领域。我是 对神经元放电感兴趣，以确保我的工作适用于更大的神经系统领域 使用尖峰，而不是分级的潜力。