The neural basis of Bayesian sensorimotor integration

All nervous systems must operate under conditions of uncertainty. It is thought that one way the brain deals with this fact is to create prior expectations of the world based on experience, and integrate those prior expectations with incoming sensory input to decide and take an action. Combining prior expectations with incoming information is known in the field as Bayesian integration. How neural circuits perform such Bayesian calculations, however, is unknown. Working in monkeys, we will combine a complex task with state-of-the-art technology to record from multiple neurons simultaneously and mathematical tools to uncover the principles by which neural circuits perform Bayesian integration. The task is as follows. The monkeys will observe two flashes of light, and then asked to reproduce the interval between flashes with a tap of the finger or a quick movement of the eye. The Bayesian calculation here is to integrate the memory of the interval (the prior expectation) with sensory information (the elapsed time between flashes). We will then use electrodes to record from brain areas thought to be involved in this task and analyze the activity of those neurons using sophisticated mathematical tools. With this approach, we can make specific predictions about how the neurons should behave based on our mathematical models, and test those predictions with experiments. More generally, our results will shed light on how animals integrate internal and external information to generate flexible, goal-directed actions.