Abstract
There are multiple indications that the nervous system of animals tunes muscle output to exploit natural dynamics of the elastic locomotor system and the environment. This is an advantageous strategy especially in fast periodic movements, since the elastic elements store energy and increase energy efficiency and movement speed. Experimental evidence suggests that coordination among joints involves proprioceptive input and neuromodulatory influence originating in the brain stem. However, the neural strategies underlying the coordination of fast periodic movements remain poorly understood. Based on robotics control theory, we suggest that the nervous system implements a mechanism to accomplish coordination between joints by a linear coordinate transformation from the multi-dimensional space representing proprioceptive input at the joint level into a one-dimensional controller space. In this one-dimensional subspace, the movements of a whole limb can be driven by a single oscillating unit as simple as a reflex interneuron. The output of the oscillating unit is transformed back to joint space via the same transformation. The transformation weights correspond to the dominant principal component of the movement. In this study, we propose a biologically plausible neural network to exemplify that the central nervous system (CNS) may encode our controller design. Using theoretical considerations and computer simulations, we demonstrate that spike-timing-dependent plasticity (STDP) for the input mapping and serotonergic neuromodulation for the output mapping can extract the dominant principal component of sensory signals. Our simulations show that our network can reliably control mechanical systems of different complexity and increase the energy efficiency of ongoing cyclic movements. The proposed network is simple and consistent with previous biologic experiments. Thus, our controller could serve as a candidate to describe the neural control of fast, energy-efficient, periodic movements involving multiple coupled joints.
Highlights
During fast periodic motions, such as jumping or drumming, animals exploit the natural dynamics of their elastic locomotor systems to achieve high velocity in an energy-efficient manner (Bar-Cohen, 2011, p. 514)
We propose an algorithm that was purely derived by engineering considerations on the control of biomechanically inspired robotic systems, to describe how the central nervous system (CNS) may tune ballistic periodic movements to energy efficiency
We have previously shown that under specific intrinsic damping properties of muscles, tendons, and joints, the control of fast periodic movements can be reduced to exciting the local linear approximation of the non-linear mode of the system (Lakatos and Albu-Schäffer, 2014a; Lakatos et al, 2014)
Summary
During fast periodic motions, such as jumping or drumming, animals exploit the natural dynamics of their elastic locomotor systems to achieve high velocity in an energy-efficient manner (Bar-Cohen, 2011, p. 514). During fast periodic motions, such as jumping or drumming, animals exploit the natural dynamics of their elastic locomotor systems to achieve high velocity in an energy-efficient manner Their central nervous systems (CNSs) are able to quickly adjust the control of periodic movements that involve several joints to face changes of their environment or intrinsic body properties (Hatsopoulos and Warren, 1996; Zondervan et al, 2014). The synchronicity enables high maximum force and thereby allows to take advantage of elastic dynamics. This can increase the resulting energy efficiency and thereby movement speed. For the remainder of this article, the latter mechanism shall be denoted as intra-limb coordination
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