Neural dynamics of planned arm movements: emergent invariants and speed-accuracy properties during trajectory formation.

Abstract

A real-time neural network model, called the vector-integration-to-endpoint (VITE) model is developed and used to simulate quantitatively behavioral and neural data about planned and passive arm movements. Invariants o farm movements emerge through network interactions rather than through an explicitly precomputed trajectory. Motor planning occurs in the form of a target position command (TPC), which specifies where the arm intends to move, and an independently controlled GO command, which specifies the movement's overall speed. Automatic processes convert this information into an arm trajectory with invariant properties. These automatic processes include computation of a present position command (PPC) and a difference vector (DV). The DV is the difference between the PPC and the TPC at any time. The PPC is gradually updated by integrating the DV through time. The GO signal multiplies the DV before it is integrated by the PPC. The PPC generates an outflow movement command to its target muscle groups. Opponent interactions regulate the PPCs to agonist and antagonist muscle groups. This system generates synchronous movements across synergetic muscles by automatically compensating for the different total contractions that each muscle group must undergo. Quantitative simulations are provided of Woodworth's law, of the speed-accuracy trade-offknown as Fitts's law, of isotonic arm-movement properties before and after deafferentation, of synchronous and compensatory central-error-correction properties of isometric contractions, of velocity amplification during target switching, of velocity profile invariance and asymmetry, of the changes in velocity profile asymmetry at higher movement speeds, of the automarie compensation for staggered onset times of synergetic muscles, of vector cell properties in precentral motor cortex, of the inverse relation between movement duration and peak velocity, and of peak acceleration as a function of movement amplitude and duration. It is shown that TPC, PPC, and DV computations are needed to actively modulate, or gate, the learning of associative maps between TPCs of different modalities, such as between the eye-head system and the hand-arm system. By using such an associative map, looking at an object can activate a TPC of the hand-arm system, as Piaget noted. Then a VITE circuit can translate this TPC into an invariant movement trajectory. An auxiliary circuit, called the Passive Update of Position (PUP) model is described for using inflow signals to update the PPC during passive arm movements owing to external forces. Other uses of outflow and inflow signals are also noted, such as for adaptive linearization of a nonlinear muscle plant, and sequential readout of TPCs during a serial plan, as in reaching and grasping. Comparisons are made with other models of motor control, such as the mass-spring and minimumjerk models.