Characterizing the noise resilience of larval chemotaxis with virtual olfactory realities

摘要 为了生存，生物体必须收集有关其环境的信息，并利用这些信息来选择合适的 行为。然而，来自环境的信息往往是嘈杂的，不完整的和模糊的。目前， 没有理论或模型全面解释神经系统如何解决导航问题 噪音信息。没有这样的理论，我们就不能提高生命系统的能力， 通过处理通常可用的不完美的感官信息， 给他们. 我们建议建立一个完整的数据驱动模型，以了解神经系统如何将嘈杂的感官信息 在导航过程中进行操作选择。我们之前已经能够通过以下方式破译这一过程的各个方面： 研究黑腹果蝇幼虫--一种非常擅长 尽管只有10,000个神经元，但它仍然能够识别食物的气味。我的实验室已经开发出了 严格量化气味景观;测量神经元如何代表这些气味;自动跟踪幼虫 运动;为幼虫创造虚拟的感官现实;并改变幼虫的实时行为- 光遗传学的需求。我们最近还绘制了幼虫神经系统内的整个通路 系统在这里，我们将确定如何以及何时嘈杂的感官信息导致幼虫重新定向（停止 并转向），因为它正朝着有吸引力的气味源（趋化性）导航。我们的目标是揭露 神经机制，积累，过滤和处理嘈杂的感官证据，并使用模糊的 信息做出一致的感知决策（动作选择）。通过结合理论，实验， 和建模，我们将迭代建立一个定量模型，预测细胞和电路水平 将感觉（嗅觉）信号转换为导航决策（趋化性）的计算， 对环境干扰（噪音）的鲁棒性。

Abstract To survive, living organisms must collect information about their environment and use it to select appropriate behaviors. However, information from the environment is often noisy, incomplete and ambiguous. Currently, no theory or model comprehensively explains how nervous systems solve the problem of navigation based on noisy information. Without such a theory, we cannot improve the ability of living systems or autonomous machines to make better decisions by processing the imperfect sensory information that is typically available to them. We propose to build a complete data-driven model of how nervous systems turn noisy sensory information into action selection during navigation. We have previously been able to decipher aspects of this process by studying the Drosophila melanogaster larva — a small, transparent organism that is exceptionally good at navigating towards food odors despite having only 10,000 neurons. My lab has developed methods to rigorously quantify odor landscapes; measure how neurons represent these odors; automatically track larval movement; create virtual sensory realities for the larva; and change the real-time behavior of the larva on- demand with optogenetics. We have also recently mapped an entire pathway within the larval nervous system. Here, we will determine how and when noisy sensory information causes the larva to reorient (stop and turn) as it is navigating towards an attractive odor source (chemotaxis). Our objective is to uncover the neural mechanisms that accumulate, filter, and process noisy sensory evidence and use ambiguous information to make coherent perceptual decisions (action selection). By combining theory, experiments, and modeling, we will iteratively build a quantitative model which predicts the cellular and circuit-level computations transforming sensory (olfactory) signals into navigational decision-making (chemotaxis) that is robust to environmental disturbances (noise).