Neural Navigation: rapid learning in complex partially observed environments*

Brains arguably evolved in order to coordinate the movement of organisms in space. Much is known about how sensory stimuli are transduced into neural signals and how brains generate motor actions. However, it remains unclear how brains convert sensory and internal signals into complex sequences of actions. How do neural tissues switch between different modes of action, such as exploration of the environment vs exploitation of known direct routes to rewarding locations? How are navigation-related error signals computed? What neural constraints govern effective navigation through a complex environment? Traditionally, these questions have been difficult to address in animal subjects, due to the long training times required for even simple tasks. Here, I leverage the natural propensity of rodents to explore and memorize complex environments. Experiments in complex mazes allow multi-action sequences along direct routes between locations of interest to be learned in a single experimental session. Complementary modeling approaches, drawing inspiration from physics and machine learning, help to characterize the range of possible biological solutions to these problems and to generate predictions for the neural activities recorded from navigating rodents.