Nervous systems have evolved to combine environmental information with internal state to select and generate adaptive behavioral sequences. To better understand these computations and their implementation in neural circuits, natural behavior must be carefully measured and quantified. Here, we collect high spatial resolution video of single zebrafish larvae swimming in a naturalistic environment and develop models of their action selection across exploration and hunting. Zebrafish larvae swim in punctuated bouts separated by longer periods of rest called interbout intervals. We take advantage of this structure by categorizing bouts into discrete types and representing their behavior as labeled sequences of bout types emitted over time. We then construct probabilistic models—specifically, marked renewal processes—to evaluate how bout types and interbout intervals are selected by the fish as a function of its internal hunger state, behavioral history, and the locations and properties of nearby prey. Finally, we evaluate the models by their predictive likelihood and their ability to generate realistic trajectories of virtual fish swimming through simulated environments. Our simulations capture multiple timescales of structure in larval zebrafish behavior and expose many ways in which hunger state influences their action selection to promote food seeking during hunger and safety during satiety.

中文翻译：

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斑马鱼幼体行为的概率模型揭示了多种尺度的结构

神经系统已经进化到将环境信息与内部状态结合起来，以选择和生成适应性行为序列。为了更好地理解这些计算及其在神经回路中的实现，必须仔细测量和量化自然行为。在这里，我们收集了单个斑马鱼幼虫在自然环境中游泳的高空间分辨率视频，并开发了它们在探索和狩猎过程中的动作选择模型。斑马鱼幼虫以间断的方式游泳，中间有较长的休息时间，称为间歇时间间隔。我们利用这种结构，将回合分类为离散类型，并将其行为表示为随着时间推移发出的回合类型的标记序列。然后，我们构建概率模型（特别是标记的更新过程）来评估鱼如何根据其内部饥饿状态、行为历史以及附近猎物的位置和属性来选择回合类型和回合间隔。最后，我们通过模型的预测可能性和生成虚拟鱼在模拟环境中游动的真实轨迹的能力来评估模型。我们的模拟捕获了斑马鱼幼虫行为的多个时间尺度的结构，并揭示了饥饿状态影响其行为选择的许多方式，以促进饥饿期间的食物寻找和饱足期间的安全。