CRI: Navigation and the Hippocampus: Computational Models

IBN：9634339 PI：Dayan 像老鼠这样能以复杂方式操纵空间的动物， 获得有关布局的大量信息的能力， 他们的环境的内容，基于各种各样的线索， 不同的模式。他们还计划采取行动， 根据他们不断变化的目标，这些任务 远超目前的机器人技术。 潜在的神经基础 老鼠的这些能力开始被人们所了解。海马 这种结构的解剖学和生理学长期以来 研究，已知包含可靠报告其 在熟悉的环境中。 同步录音从 海马体中的多个细胞开始显示这些地图 是在经验中形成的。我们将联合收割机结合三种关键方法 用于脑科学-动物行为，大脑的神经记录 海马体的活动，以及计算机建模来了解 大鼠用于构建模型的自适应优化控制策略 他们的环境，并建立最佳计划。 导航是自治代理的一个关键问题。这样做的结果 工作将是大脑功能的模型， 参与学习、记忆和控制的生物结构， 空间任务。这些模型将能够集成 多传感器信息和实施优化控制。 类似 基于动物行为理论的方法已经发现 广泛应用于各种复杂的组合问题， 比如日程安排。我们将采用的强化学习方法 要了解自然导航， 在智能机器人系统开发中的应用。

IBN: 9634339 PI: Dayan Animals such as rats that manipulate space in sophisticated ways have the capacity to acquire substantial information about the layout and contents of their environments, based on a large variety of cues from different modalities. They also plan their actions to optimise the use of these environments, based on their changing goals. These tasks are far beyond current robot technology. The neural basis underlying these abilities of rats is starting to be understood. The hippocampus of a rat, a structure whose anatomy and physiology have long been studied, is known to contain cells that report reliably on its location in a familiar environment. Simultaneous recordings from multiple cells in the hippocampus are starting to show how these maps are formed during experience. We will combine three key approaches used in brain science - animal behavior, neural recording of brain activity in the hippocampus, and computer modeling to understand the adaptive optimising control strategies that rats use to build models of their environments and to construct optimal plans. Navigation is a key problem for autonomous agents. The result of this work will be models of brain function which exploit the capacities of the biological structures involved in learning, memory and control in spatial tasks. These models will be capable of integrating poly-sensory information and implementing optimising control. Similar approaches based on theories of animal behavior are already finding wide application in a variety of difficult combinatorial problems, such as scheduling. The reinforcement learning methods we will adopt to understand natural navigation should have direct engineering application in the development of intelligent robotic systems.