Developing Quantitative Methods to Reveal Neural Circuit Dynamics

项目总结/摘要 神经活动模式是大脑信息处理的基础。迄今为止，大多数工作都集中在 在不同的计算阶段，通过观察大脑中不同的区域-一次一个。我们 我认为图论的技术可以帮助我们更好地理解信息是如何被处理的 由整个神经元群体组成。为此，我们使用线虫秀丽隐杆线虫来研究 在大多数神经系统中同时处理生态相关信号。具体地说， 我们将记录蠕虫头部所有神经元的活动， 当我们将动物暴露在一种天生吸引人的气味--双乙酰中时，然后我们将测试如何 表示在两种行为状态下发生变化-适应后，此时蠕虫不再 发现双乙酰有吸引力，当C.当它再次找到双乙酰时， 吸引人.我们将在转基因蠕虫中完成所有这些，这将使我们能够通过名称识别所有神经元， 从而根据已知的神经元之间的解剖学连接来分析我们的结果。工作 我们正准备提交出版，已经建立了一个图论特征可以识别 刺激的效价，即它是吸引还是排斥，我们将确定哪些神经元 正在推动这一功能的改变。最后，我们将光遗传学测试我们的分析预测 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 可能代表正和负价，它们的激活可能会诱导正向，或 分别向后移动。例如，如果一个神经元提供了 代表任何价的神经元（即它在这些神经元之间的最短路径上）和运动神经元 指挥中间神经元，那么我们可能会认为它促进了信息的传递， 抑制它会延迟动物寻找气味的行为。这项工作的未来扩展将 联合收割机结合图形理论和信息理论，以了解如何有效地神经元处理， 传输信息。重要的是，这个领域，称为网络编码，提出了一种有效的方法， 发送信息的目的是允许下游节点解码沿着沿着 路径在我的博士后工作中，当我获得新理论和新神经系统的经验时， 使用尖峰神经元，我寻求开发神经科学的网络编码领域。我是 我对尖峰神经元感兴趣，以确保我的工作适用于更大的神经系统领域， 使用尖峰电位而不是分级电位。

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.