The Influence of Neuronal Variability on Perception and Behavior

项目摘要 我们对世界的反应不仅仅是我们的感官探测到的东西的总和，还包括我们的 大脑选择代表这些探测，并对它们采取行动。然而，关于感官的知识 输入被我们的大脑解释为产生行为输出是缺乏的。与简单的反射不同， 我们的大多数行为都是由外部世界的内部表现所支配的。这个内部 状态不是静态的，而是在清醒和睡眠期间都显示出动态的活动。简而言之，我们是 总是在思考。即使在感觉缺失的情况下，我们大脑的几乎所有区域都保持活跃。 因此，预测感官输入的行为输出的中心挑战是我们的 对世界的感知是动态的和高度可变的，这使得许多行为变得不可预测。 对复杂大脑内部动力如何产生的功能基础的理解是有限的。 然而，动态的内部状态也会影响简单动物的感知和行为，例如 秀丽隐杆线虫。它是目前唯一一种我们有每个神经元的详细地图的生物体 和突触。我们可以同时操控和监测大脑中每个神经元的活动。 这种在完全绘制的大脑中操纵特定神经元的精妙能力使线虫成为理想的 了解简化系统中静息状态动力学的复杂性。我的长期生活 目标是了解神经回路如何在不同的时间尺度上产生静息状态动力学，以及 这些状态如何影响感知和行为。了解这一过程将提供一个 了解学习过程的基础，以及控制行为方式的原则 新奇的事物产生并适应环境。

Project Summary Our response to the world is not simply the sum of what our sensory organs detect, but also how our brain chooses to represent these detections, and act upon them. However, knowledge of how sensory inputs are interpreted by our brains to yield behavioral outputs is lacking. Unlike simple reflexes, the majority of our behaviors are governed by an internal representation of the external world. This internal state is not static; but shows dynamic activity during both awake and sleep periods. In short, we are always thinking. Even in sensory deprived contexts, nearly all regions of our brain remain active. Therefore, the central challenge with predicting behavioral output from sensory input is that our perception of the world is dynamic and highly variable, rendering many behaviors unpredictable. Understanding the functional underpinnings of how internal dynamics arise in complex brains is limited. However, dynamic internal states also influence perception and behavior in simple animals such as Caenorhabditis elegans. It is currently the only organism where we have a detailed map of every neuron and synapse. We can simultaneously manipulate and monitor the activity of every neuron in the brain. This exquisite ability to manipulate specific neurons in a fully mapped brain makes C. elegans the ideal organism to understand the complexity of resting state dynamics in a reduced system. My long-term goal is to understand how neural circuits generate resting state dynamics on different timescales, and how these states influence perception and behavior. Understanding this process will provide a foundation for understanding the learning process, and the principles that govern how behavioral novelty arises and adapts to the environment.