Neuromorphic meets neuromechanics, part II: the role of fusimotor drive

Abstract

Objective We studied the fundamentals of muscle afferentation by building a neuro-mechano-morphic system actuating a cadaveric finger. This system is a faithful implementation of the stretch reflex circuitry. It allowed the systematic exploration of the effects of different fusimotor drives to the muscle spindle on the closed-loop stretch reflex response. Approach As in Part I of this work, sensory neurons conveyed proprioceptive information from muscle spindles (with static and dynamic fusimotor drive) to populations of α-motor neurons (with recruitment and rate coding properties). The motor commands were transformed into tendon forces by a Hill-type muscle model (with activation-contraction dynamics) via brushless DC motors. Two independent afferented muscles emulated the forces of flexor digitorum profundus and the extensor indicis proprius muscles, forming an antagonist pair at the metacarpophalangeal joint of a cadaveric index finger. We measured the physical response to repetitions of bidirectional ramp-and-hold rotational perturbations for 81 combinations of static and dynamic fusimotor drives, across four ramp velocities, and three levels of constant cortical drive to the α-motor neuron pool. Results We found that this system produced responses compatible with the physiological literature. Fusimotor and cortical drives had nonlinear effects on the reflex forces. In particular, only cortical drive affected the sensitivity of reflex forces to static fusimotor drive. In contrast, both static fusimotor and cortical drives reduced the sensitivity to dynamic fusimotor drive. Interestingly, realistic signal-dependent motor noise emerged naturally in our system without having been explicitly modeled. Significance We demonstrate that these fundamental features of spinal afferentation sufficed to produce muscle function. As such, our neuro-mechano-morphic system is a viable platform to study the spinal mechanisms for healthy muscle function — and its pathologies such as dystonia and spasticity. In addition, it is a working prototype of a robust biomorphic controller for compliant robotic limbs and exoskeletons.

Physiological tremor increases when skeletal muscle is shortened: implications for fusimotor control

“…We have even recently documented that increases in γ‐static fusimotor drive can actually decrease the stretch reflex amplitude (Jalaleddini et al . ). Our results here now provide further evidence to suggest that the amplitudes of the stretch reflex and physiological tremor are separable — likely because they are driven by distinct, but complementary and sometimes overlapping, afferent information.…”

Perspectives on the modeling of the neuromusculoskeletal system to investigate the influence of neurodegenerative diseases on sensorimotor control

“…However, the great challenge in working with muscle spindle models is the setting of the fusimotor drive (static and dynamic), which is mostly unknown in humans and primates. Most of the studies adopt ad-hoc values for the fusimotor inputs to reproduce experimental data from cats and humans (Elias et al, 2014;Jalaleddini et al, 2017a;2017b;Niu et al, 2017). Albeit a comprehensive model of Golgi tendon organs (GTOs) has been proposed in the literature , multi-scale neuromusculoskeletal models have adopted phenomenological nonlinear models for GTO activity produced in response to force modulation (Lin and Crago, 2002).…”

On neuromechanical approaches for the study of biological and robotic grasp and manipulation

“…Know how to solve every problem that has been solved.' 3 In our context, it can be taken to mean that, if we have over one hundred years of sensorimotor neuroscience since Sir Charles Sherrington [74], and if the principles we have deduced are sound, then we should be able to build components that embody those mechanisms in such a way that when assembled they behave like biological systems [75,76]. One example of such a neuromorphic approach uses ultra-fast computer processors to simultaneously implement populations of autonomous, interconnected spiking neurons in real time that follow Hodgkin-Huxley rules of how action potentials in neurons are initiated and propagated [77].…”

“…to explore emergent behavior at truly multiple scales. So far, this approach has allowed us to begin to understand cardinal features of afferent muscles of human fingers to replicate fundamental features of healthy muscle tone, hypo and hypertonia [76].…”

“…Bayesian inference [186,187] or stochastic control [143] are formal approaches to describe the emergence of probability density functions of the mapping from perception/intention to action in the presence of unavoidable uncertainty, noise, risk and variability in the real world. Moreover, there are probabilistic approaches that can be used with populations of spiking neurons to produce physical behavior in a cost-agnostic, emergent, model-free way [188,189,75,76].…”