A hypothetical neural network model for generation of human precision grip.

Humans can stably hold and skillfully manipulate an object by coordinated control of a complex, redundant musculoskeletal system. However, how the human central nervous system actually accomplishes precision grip tasks by coordinated control of fingertip forces remains unclear. In the present study, we aimed to construct a hypothetical neural network model that can spontaneously generate humanlike precision grip. The nervous system was modeled as a recurrent neural network model prescribing kinematic and kinetic constraints that must be satisfied in precision grip tasks in the form of energy functions. The recurrent neural network autonomously behaves so as to decrease the energy functions; therefore, given the estimated mass and center-of-mass location of the target object, the nervous system model can spontaneously generate muscle activation signals that achieve stable precision grips due to dynamic relaxation of the energy functions embedded in the nervous system. Fingertip forces are modulated by sensory information about slip between the object and fingertips. A two-dimensional musculoskeletal model of the human hand with a thumb and an index finger was constructed. Forward dynamic simulation of the precision grip was performed using the proposed neural network model. Our results demonstrated that the proposed neural network model could stably pinch and successfully hold up the object in various conditions, including changes in friction, object shape, object mass, and center-of-mass location. The proposed hypothetical neuro-computational model may possibly explain some aspects of the control strategy humans use for precision grip.