How do animals learn the structure of their natural environment?

项目摘要/摘要 大多数动物生活在复杂多变的环境中。为了寻找他们需要的食物、住所或躲避威胁 学习如何以一种适应性的方式驾驭这些环境。由于自然环境不会在 以完全随机的方式，有可能识别出规律性，让动物能够预测这样的变化。有能力 环境中潜在的结构化关系被称为结构学习。这一能力是 智力的基本方面，允许在一个人的经验之外进行概括和推断。然而，它的 神经机制尚不清楚。这项建议的主要目的是阐明神经回路机制。 以老鼠觅食和社会行为为模型的结构学习。当动物与它的环境或环境互动时 其他同种情况，海马区神经元和相关的皮质区域代表相同的外部变量火 一起形成一个功能组件。程序集的顺序激活提供了一种编码关系 世界地图，结合了空间、社会和其他类型的信息，可以灵活地重新配置以跟踪 改变。该编码方案也是预测性的，因为序列中初始组件的激活可以招募 后续事件，预测未来事件的发生。使神经元序列能够支持结构学习 他们还需要提供一种对新情况进行推论和概括的方法。这一过程包括确定 潜在的原则形成经验，并将其应用于新的情况。我将测试神经细胞的假设 序列是一种通过泛化来支持结构学习和推理的机制。在他们的自然环境中 环境中，老鼠成群结队地生活，在广阔的地区觅食，这种复杂性是普通动物无法捕捉的 实验室化验。如果需要不同的细胞来编码动物在其自然状态下经历的每个偶然性事件 环境，正如该领域的主要游行所提出的那样，它将需要比大脑拥有更多的神经元。一种方法来 解决这一问题，就是利用结构学习对常见的潜在特征进行泛化，去掉不相关的信息。 这项拟议的工作将研究支持动物学习潜伏期的能力的神经回路机制。 通过构建内部预测模型和推广来构建其自然环境的结构，以及他们如何使用这些 用于指导灵活行为的表示法。我们将解决阻碍这些方面取得进展的两个主要障碍 问题。其一是需要长期稳定地记录大脑各区域的神经元，并配合特定的操作 在不限制动物在大空间中的行为或与其他同种动物相互作用的情况下，它们之间的相互作用。这个 第二，开发行为范式，捕捉自然界中社会和觅食行为的复杂性 并且容易受到神经记录的影响。我们将部署几项技术创新，以克服当前 限制(目标0)，并应用它们来确定支持社会空间结构学习的神经回路机制 大鼠(AIM1)。在AIM2中，我们将对在大型室外围栏中觅食的大鼠进行神经记录，以确定如何 在实验室环境中确定的机制可以转化为更自然的条件。在目标3中，我们将研究老鼠是如何 学习复杂的社会结构以及这如何影响它们在自然户外环境中的觅食行为。

Project Summary/Abstract Most animals live in complex, changing environments. In order to search for food, shelter or to scape threats they need to learn how to navigate those environments in an adaptative manner. Since natural environments do not change in a complete random manner, it is possible to identify regularities, allowing animals to predict such changes. The ability to abstract latent structured relationships in the environment is known as structure learning. This ability is one of the fundamental aspects of intelligence, allowing to generalize and make inferences beyond one’s experience. However, its neural mechanisms are not known. The main goal of this proposal is to elucidate the neural circuit mechanisms of structure learning using rat foraging and social behavior as a model. As an animal interacts with its environment or other conspecifics, neurons in the hippocampus and associated cortical areas representing the same external variable fire together forming a functional assembly. The sequential activation of assemblies offers a mechanism to encode a relational map of the world that combines spatial, social and other types of information and can be flexibly reconfigured to track changes. This coding scheme is also predictive, since the activation of an initial assembly in the sequence can recruit subsequent ones, anticipating the occurrence of future events. For neuronal sequences to be able to support structure learning they also need to offer a means to perform inferences and generalize to new situations. This process involves identifying underlying principles form experience and applying them to novel situations. I will test the hypothesis that neuronal sequences are a mechanism that supports structure learning and inference through generalization. In their natural environments, rats live in large colonies and forage over extended areas, a complexity that is not captured by common laboratory assays. If different cells would be necessary to encode each contingency experienced by an animal in its natural environment, as the dominant parading in the field proposes, it would require more neurons that its brain has. A way to solve this problem, is to use structure learning to generalize common latent features and discard irrelevant information. The proposed work will investigate the neural circuit mechanisms that support the ability of animals to learn the latent structure of their natural environments by constructing internal predictive models and generalizing, and how they use such representations to guide flexible behavior. We will solve the two main obstacles that have prevented progress on these questions. One is the need for long-term stable recordings of neurons across brain areas together with specific manipulations of their interactions, without restricting animal behavior in large spaces or while interacting with other conspecifics. The second is to develop behavioral paradigms that capture the complexity of social and foraging behavior in natural environments and are amenable to neural recordings. We will deploy several technical innovations to overcome current limitations (AIM 0) and apply them to determine the neural circuit mechanisms that support socio-spatial structure learning in rats (AIM1). In AIM2 we will perform neural recordings in rats foraging in large outdoor enclosures to determine how the mechanisms identified in laboratory settings translate to more natural conditions. In AIM 3 we will investigate how rats learn complex social structures and how this affects their foraging behavior in naturalistic outdoor environments.