A latent variable model for quantifying social behavior in rodents

项目总结 量化哺乳动物的自然行为，包括社会互动的计算方法，对于 发展对行为的神经基础的复杂理解。然而，对行为的完整描述 它包含的不仅仅是动物的行为。外部暗示(如社交伙伴的行为)会推动我们的 行为反应，以及我们对这些提示的反应可能取决于上下文、我们的内部心理状态，以及 以前的工作经验。我们可能会在感觉安全的时候接近某个人，或者在我们感到安全的时候攻击同一个人 受到威胁。由此产生的复杂性使自然行为--尤其是社会互动-- 学习起来很有挑战性。为了克服这一障碍，我建议开发广泛适用的模型来预测自然 以及基于外部线索和内部状态变化的小鼠社会行为动力学。这些型号将 使用无监督学习技术来量化和预测可解释的行为的复杂模式 同时将社会行为与多个时间尺度上神经活动的变化联系起来。加在一起，这些 模型将提供一种前所未有的视角，了解不同的神经群体如何编码 随着时间的推移，塑造社会行为。第一个目标是将一组日益复杂的数据集与 灵活的潜态模型，描述了自然和社会行为是如何随着以下因素而产生的 外部线索和时变的内部状态。在第二个目标中，我将把这个建模框架应用于钙 伏隔核、纹状体尾部多巴胺能投射的记录 外侧缰核中的谷氨酸能细胞体--所有神经群体都被证明在社会背景下做出反应。 我将确定这些神经群体如何不同地编码感觉输入、内部状态和行为 产出。我还将研究每个神经种群中的活动如何与不同的 行为和内部状态，以及这些表征如何随着经验的变化而变化。拟议的工作将 通过应用新的计算工具和复杂的、无监督的行为实现新突破 量化方法，以发现塑造自然行为的内部和外部变量以及 社会互动的潜在神经关联。总而言之，新的计算建模技术 AM提议将推进NIMH理论和计算神经科学计划的几个目标： 它们(1)包含不同的分析水平，(2)将神经元和行为过程联系起来，(3)增强预测 高分辨率行为数据以及神经分析单元，以及(4)提供有效的解释 对其结果进行解释的技术和方法。

PROJECT SUMMARY Computational methods for quantifying mammalian natural behavior, including social interactions, are crucial for developing a sophisticated understanding of the neural basis of behavior. Yet a full description of behavior consists of much more than an animal’s actions. External cues (such as the actions of a social partner) drive our behavioral responses, and our responses to those cues can depend on context, our internal mental state, and prior experience. We may approach an individual when we feel safe, or attack that same individual when we feel threatened. The resulting complexity makes natural behavior — and social interactions in particular — challenging to study. To overcome this barrier, I propose to develop broadly applicable models to predict natural and social behavioral dynamics in mice based on changing external cues and internal states. These models will use unsupervised learning techniques to quantify and predict complex patterns of behavior in an interpretable manner while linking social behaviors to changes in neural activity across multiple timescales. Together, these models will provide an unprecedented view of how different neural populations encode the internal states that shape social behaviors as they unfold over time. The first aim is to fit a set of increasingly complex datasets with flexible latent-state models that describe how natural and social behaviors arise in response to factors such as external cues and time-varying internal states. In the second aim, I will apply this modeling framework to calcium recordings in dopaminergic projections to the Nucleus Accumbens and Tail of the Striatum as well as glutamatergic cell bodies in the Lateral Habenula — all neural populations shown to respond in social contexts. I will determine how these neural populations differentially encode sensory inputs, internal states, and behavioral outputs. I will also examine how the activity in each neural population correlates with transitions between different behaviors and internal states and how these representations change with experience. The proposed work will break new ground by applying novel computational tools and sophisticated, unsupervised behavioral quantification methods to discover the internal and external variables that shape natural behaviors as well as the underlying neural correlates of social interactions. Together, the new computational modeling techniques that I am proposing will advance several goals of the NIMH Theoretical and Computational Neuroscience Program: they (1) contain distinct levels of analysis, (2) link neuronal and behavioral processes, (3) enhance predictions of high-resolution behavioral data along with neural units of analysis, and (4) provide effective explanatory techniques and methods of interpretation for their results.