Nervous systems sense, communicate, compute, and actuate movement, using distributed hardware with tradeoffs in speed and accuracy. The resulting sensorimotor control is nevertheless remarkably fast and accurate due to highly effective layered architectures. However, such architectures have received little attention in neuroscience due to the lack of theory that connects the system and hardware level speed-accuracy tradeoffs. In this paper, we present a theoretical framework that connects the speed-accuracy tradeoffs of sensorimotor control and neurophysiology. We characterize how the component SATs in spiking neuron communication and their sensory and muscle endpoints constrain the system SATs in both stochastic and deterministic models. The results show that appropriate speed -accuracy diversity at the neurons/muscles levels allow nervous systems to improve the speed and accuracy in control performance despite using slow or inaccurate hardware. Then, we characterize the fundamental limits of layered control systems and show that appropriate diversity in planning and reaction layers leads to both fast and accurate system despite being composed of slow or inaccurate layers. We term these phenomena “Diversity Sweet Spots.” The theory presented here is illustrated in a companion paper, which introduces simple demos and a new inexpensive and easy-to-use experimental platform.
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Experimental and educational platforms for studying architecture and tradeoffs in human sensorimotor control
This paper describes several surprisingly rich but simple demos and a new experimental platform for human sensorimotor control research and also controls education. The platform safely simulates a canonical sensorimotor task of riding a mountain bike down a steep, twisting, bumpy trail using a standard display and inexpensive off-the-shelf gaming steering wheel with a force feedback motor. We use the platform to verify our theory, presented in a companion paper. The theory tells how component hardware speed-accuracy tradeoffs (SATs) in control loops impose corresponding SATs at the system level and how effective architectures mitigate the deleterious impact of hardware SATs through layering and “diversity sweet spots” (DSSs). Specifically, we measure the impacts on system performance of delays, quantization, and uncertainties in sensorimotor control loops, both within the subject's nervous system and added externally via software in the platform. This provides a remarkably rich test of the theory, which is consistent with all preliminary data. Moreover, as the theory predicted, subjects effectively multiplex specific higher layer planning/tracking of the trail using vision with lower layer rejection of unseen bump disturbances using reflexes. In contrast, humans multitask badly on tasks that do not naturally distribute across layers (e.g. texting and driving). The platform is cheap to build and easy to program for both research and education purposes, yet verifies our theory, which is aimed at closing a crucial gap between neurophysiology and sensorimotor control. The platform can be downloaded at https://github.com/Doyle-Lab/WheelCon.
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- Award ID(s):
- 1735003
- NSF-PAR ID:
- 10155677
- Date Published:
- Journal Name:
- 2019 American Control Conference
- Page Range / eLocation ID:
- 483 to 488
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/ACC.2019.8814470
