Abstract

Neural circuits evolved to deal with the complex demands of a dynamic and uncertain world. To understand dynamic neural processing underlying natural behaviour, we use a continuous-time foraging task in which humans and macaques use a joystick to steer and catch flashing fireflies in a virtual environment. In order to solve the task, monkeys must dynamically update their position estimates by integrating optic flow generated by self-motion, a process known as ‘path integration’. We introduce a probabilistic framework to refute a popular account of path integration that attributes biases to forgetful integration. We instead find that such biases are explained naturally by an optimal strategy that maximizes rewards while accounting for prior expectations about our own movements. Interestingly, both humans and monkeys continue to track the target even after it was long gone, such that variability in subjects’ eye positions mirrors their behavioral variability. Our results suggest that the output of integration may be embedded in the brain’s oculomotor circuit, such that the eye position provides a dynamic readout of one’s distance to target during visual path integration. We use multi-electrode array and laminar probes to sample the activity of a large number of neurons in the posterior parietal cortex and find that different neurons are active during different epochs of integration. Neurons exhibit rich temporal diversity such that the integration dynamics appear embedded in the dynamical pattern of population activity. We are currently applying statistical techniques to characterise the precise dynamics of population activity to understand the associated neural computations.