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Nervous systems sense, communicate, compute, and actuate movement using distributed components with severe trade-offs in speed, accuracy, sparsity, noise, and saturation. Nevertheless, brains achieve remarkably fast, accurate, and robust control performance due to a highly effective layered control architecture. Here, we introduce a driving task to study how a mountain biker mitigates the immediate disturbance of trail bumps and responds to changes in trail direction. We manipulated the time delays and accuracy of the control input from the wheel as a surrogate for manipulating the characteristics of neurons in the control loop. The observed speed–accuracy trade-offs motivated a theoretical framework consisting of two layers of control loops—a fast, but inaccurate, reflexive layer that corrects for bumps and a slow, but accurate, planning layer that computes the trajectory to follow—each with components having diverse speeds and accuracies within each physical level, such as nerve bundles containing axons with a wide range of sizes. Our model explains why the errors from two control loops are additive and shows how the errors in each control loop can be decomposed into the errors caused by the limited speeds and accuracies of the components. These results demonstrate that an appropriate diversity in the properties of neurons across layers helps to create “diversity-enabled sweet spots,” so that both fast and accurate control is achieved using slow or inaccurate components.

 

Soaring birds often rely on ascending thermal plumes in the atmosphere as they search for prey or migrate across large distances. The landscape of convective currents is turbulent and rapidly shifts on timescales of a few minutes as thermals constantly form, disintegrate, or are transported away by the wind. How soaring birds find and navigate thermals within this complex landscape is unknown. Reinforcement learning can be used to find an effective navigational strategy as a sequence of decisions taken in response to environmental cues. Reinforcement learning was applied to train gliders in the field to autonomously navigate atmospheric thermals. Gliders of two-meter wingspan were equipped with a flight controller that enabled an on-board implementation of autonomous flight policies via precise control over their bank angle and pitch. Learning is severely challenged by a multitude of physical effects and the unpredictability of the natural environment. A navigational strategy was determined solely from the experiences collected over several days in the field using exploratory behavioral policies. Bird-like performance was achieved and several viable biological mechanosensory cues were identified for soaring birds, which are also directly applicable to the development of autonomous soaring vehicles.