Neural circuitry in motion: Computational modelling of motor cortical dynamics to understand human movement

Abstract

Movement of the human body arises from a complex interaction between the activity of the nervous system and the biomechanics of the musculoskeletal system. How neurons of the brain and spinal cord produce purposeful, coordinated movements is a fundamental question in neuroscience and motor control research. Computational models offer a valuable tool to test theories and mechanisms underlying the challenge of motor control. The primary motor cortex is the area of the brain with the most direct influence and connections to producing voluntary activity in limbs. However, a consensus on the link between motor cortex activity and the production of muscle activity remains elusive. Spiking neural networks (SNNs) represent the biological mechanisms of communication between neurons via action potentials. A SNN model containing over 38,000 neurons and 160 million synapses was developed to represent a 1 mm2 surface area of the motor cortex. The neural network model used realistic, physiological parameters and connectivity to replicate the spontaneous firing behaviour of populations of neurons in the motor cortex. A model of transcranial magnetic stimulation (TMS) was also applied to the motor cortex model, resulting in the generation of highfrequency (I-waves) at the spinal cord level, matching experimental observations. In addition, the model was coupled with a musculoskeletal model of the upper limb to simulate muscle contraction and multi-body dynamics, via a simple spinal cord circuit controlling extensor and flexor muscles, showing the feasibility of coupled brain-body models. The motor cortex model is presented within a larger framework of modelling the connection from brain to muscle that incorporates feedback pathways including muscle spindles and Golgi-tendon organs, in addition to detailed muscle models. The framework of this computational modelling approach uses multi-scale, multi-modal modelling fitted where possible to experimental data to enable the observation of emergent patterns of behaviour within the motor system. Using interdisciplinary computational models to understand the neuromusculoskeletal system is widely applicable and can be developed in conjunction with experimental work and hypotheses of motor control.