Closed-loop control of neural dynamics and action selection in C elegans

项目总结/摘要 为了应对新的感官刺激，动物必须决定下一步做什么。逃避行为 是研究行为的神经生物学基础的极好模型， 重要的和可解释的。在这里，我们建议研究神经因素影响的概率 行为，其中C。elegans逃避厌恶感觉神经元ASH的刺激。另外还 将确定行为中有多少变化是由正在进行的全局神经动力学（即， 时变的活动模式，编码主要行为，如向前，向后，背， 腹部爬行在动力学过程中，一个给定的神经元会受到不同程度的兴奋和抑制 这可能会导致看似概率性的行为。 在具有细胞分辨率的全脑成像期间， 同时，我们将光遗传学刺激神经元活动的签名期间， 使用闭环钙成像定量和光遗传学与爬行行为相关 刺激平台来扰乱动力学。我们将描述刺激强度和动力学 相互作用以激活感觉和下游神经元。我们还将研究一个变量， 当蠕虫从没有明显动态的状态受到干扰时出现的逆转行为， 睡吧我们将描述神经元如何响应不同强度的刺激和适应生理 数据到与激活函数和动作选择相关的模型。 综合起来，这些实验使用一个简单的动物模型来阐明如何诱发 神经元活动由整个网络中的持续模式调节。这些实验 强调感觉和运动表征的分布式观点，并探讨其对 神经系统中的一个高度相互关联的动力学。本建议书中的研究和培训计划 是实验和计算的混合体，必须由两个领导人在每个领导人的指导下， 在著名的合作加州大学旧金山弗朗西斯科的学科。

Project Summary/Abstract In response to new sensory stimuli, an animal must decide what to do next. Escape behaviors are excellent models with which to study the neurobiological basis of behavior because they are essential and interpretable. Here we propose to study neural factors which influence a probabilistic behavior where C. elegans escapes stimulation of an aversive sensory neuron ASH. Specifically we will determine how much variance in behavior is accounted for by ongoing global neural dynamics i.e. time-varying patterns of activity which encode major behaviors such as forward, backward, dorsal, and ventral crawling. During dynamics, a given neuron receives different levels of excitation and inhibition from its synaptic partners, which may contribute to behaviors seeming probabilistic. During whole-brain imaging with cellular resolution to access the activity of the entire brain of the animal simultaneously, we will optogenetically stimulate neurons during signatures of activity correlated with crawling behaviors using a closed-loop calcium imaging quantification and optogenetic stimulation platform to perturb dynamics. We will characterize how stimulus intensity and dynamics interact to activate sensory and downstream neurons. We will also examine a variable forward or reversal behavior which emerges when worms are disturbed from a state with no obvious dynamics, sleep. We will characterize how neurons respond to different intensities of stimulation and fit physiology data to a model relating activation function and action selection. Taken together these experiments use a simple animal model to shed light on how evoked neuronal activity is modulated by ongoing patterns throughout the network. These experiments emphasize a distributed view of sensory and motor representations and explore its implications on dynamics in a highly interconnected nervous system. This research and training plan in this proposal is decidedly hybrid experimental and computational, necessarily guided by two leaders in each discipline at the famously collaborative University of California, San Francisco.