Neural mechanisms of visual motion extrapolation

Because neural processing takes time, the brain only has delayed access to sensory information. When trying to localize moving objects this is problematic, as an object will have moved on by the time its position has been determined. Here, we consider predictive motion extrapolation as a fundamental delay-compensation strategy employed by the brain. From a population-coding perspective, we outline how extrapolation can be achieved by a forwards shift in the ‘population position estimate’ – the represented position of an object, encoded in distributed neural activity. We identify three general mechanisms underlying such shifts, involving asymmetries in excitation, inhibition, and connectivity. We consider specific examples in different visual regions, as well as how motion extrapolation can be achieved during inter-regional signaling, and how asymmetric connectivity patterns which support extrapolation can emerge spontaneously from local synaptic learning rules. Finally, we consider how more abstract ‘model-based’ predictive strategies might be neurally implemented. Overall, we present a general framework for understanding how the brain determines the real-time position of moving objects, despite neural delays.