How do animals learn the structure of their natural environment?

项目总结/摘要 大多数动物生活在复杂多变的环境中。为了寻找食物，庇护所或躲避威胁， 来学习如何适应这些环境。由于自然环境不会在一个 完全随机的方式，就有可能识别出突变，使动物能够预测这种变化。的能力 环境中抽象的潜在结构化关系被称为结构学习。这种能力是 智能的基本方面，允许概括和作出超出个人经验的推论。但其 神经机制尚不清楚。这项建议的主要目标是阐明的神经回路机制， 结构学习使用大鼠觅食和社会行为作为模型。当动物与环境相互作用时， 其他同种，海马和相关皮质区域中的神经元代表相同的外部变量火 一起形成功能组件。程序集的顺序激活提供了一种对关系 一幅世界地图，结合了空间、社交及其他类型的信息，可灵活地重新配置以跟踪 变化这种编码方案也是预测性的，因为序列中初始组装的激活可以募集 预测未来事件的发生。为了让神经元序列能够支持结构学习 他们还需要提供一种执行推理并概括新情况的方法。这一过程包括识别 基本原则形成经验，并将其应用于新的情况。我将检验神经元 序列是一种通过泛化支持结构学习和推理的机制。在它们的自然 环境中，老鼠生活在大的殖民地和饲料在扩大地区，一个复杂的是没有捕捉到的共同 实验室化验。如果不同的细胞将是必要的编码每一个偶然性所经历的动物在其自然的 正如该领域的主流观点所提出的那样，它需要比大脑更多的神经元。一种方法来 解决这一问题的方法是利用结构学习来概括共同的潜在特征，并丢弃不相关的信息。 这项拟议的工作将调查支持动物学习潜在能力的神经回路机制。 通过构建内部预测模型和归纳，以及他们如何使用这些模型， 以指导灵活的行为。我们将解决阻碍在这些问题上取得进展的两个主要障碍， 问题.一个是需要长期稳定的记录整个大脑区域的神经元以及特定的操作 它们的相互作用，而不限制动物的行为在大空间或与其他同种相互作用。的 第二是发展行为范式，捕捉自然界中社会和觅食行为的复杂性， 环境和服从神经记录。我们将部署几项技术创新，以克服目前的 限制（AIM 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（AIM 1）。在AIM 2中，我们将对在大型户外围栏中觅食的大鼠进行神经记录，以确定 在实验室环境中确定的机制转化为更自然的条件。在AIM 3中，我们将研究大鼠如何 学习复杂的社会结构，以及这如何影响它们在自然的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.