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Author(s): Bullock, D. | Grossberg, S. |
Year: 1988
Citation: Psychological Review, 95 , 49-90
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.I nvariants of arm movementse mergeth rough network interactions rather than through an explicitly precomputed trajectory. Motor planning occurs in the form of a targei position command (TPC). which specifies where the arm intends to move. and an independently controlled GO command. ,, hich specifiest he movement soveralls peed.A utomatic processecso nvert this information into an arm trajectory with invariant properties.T hese automatic processesin clude computation of a present position command (PPC) and a difference vector (DV). The DV is the difference bet een 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 oodworth.s law. of the speed-accuracyt rade-offk nown as Fitts s law. of isotonic arm-movementp ropertiesb elore and after deafferentation. of synchronous and compensatory ""central-error-correction properties of isometric contractions. of velocity amplification during target sy, itching. of velocity profile invariance and asymmetry. of the changesin velocity profile asymmetry at higher movements peeds.o f the automatic compensation for staggered onset times of synergetic qluscles. 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 bet""een the eye-head system and the hand-arm system. By using such an associativem ap. looking at an objel. tc an activatea 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 P:lSsiveU pdate of Position (PUP) model is describedf or using inflow signals to update the PPC during passive arm movements o.ing 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 ofTPCs 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.
Topics:
Biological Learning,
Machine Learning,
Models:
Other,
