CRI: Navigation and the Hippocampus: Computational Models

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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.