Dissecting navigation and the general logic of episodic state computation

  • Awardees
  • Lisa Giocomo, Ph.D. Stanford University
  • Dora Angelaki, Ph.D. Baylor College of Medicine
  • Thomas R. Clandinin, Ph.D. Stanford University
  • Surya Ganguli, Ph.D. Stanford University
  • Jennifer Raymond, Ph.D. Stanford University
Year Awarded


Imagine you are rowing a boat in a river. If the boat stops moving relative to the shore, you might conclude that the river’s current has picked up and decide to row harder. That decision results from a combination of sensory information and internal predictions. Your brain compared your actual position to your expected position given how fast you were rowing and concluded that the slowing of the boat violated your sensory expectations. Every time we navigate through the world, we not only carry out the current movement but also predict how our sensory environment should change when we move. How the brain does this is largely unknown. We hypothesize that you can use your past position and current velocity to create what we call your ‘episodic state’ — the minimum knowledge of the past and present needed to predict the sensory experience of the immediate future. We will test this hypothesis by recording activity in many neurons, in both mice and flies, as the animals navigate a virtual reality environment. With virtual reality, we can create situations in which the laws of physics hold or are violated. For example, physics dictates that if you move the same amount forward as backward at the same speed, you should end up in the same place. We can break this rule and see how the animal — and its brain — responds. This setup allows us to determine how the brain computes its position and velocity, how it creates a representation of its episodic state and how it uses this information to predict future sensory feedback. This work will provide general insights into how any animal makes predictions, including humans.

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