Self-Organizing Neural Networks for Spatial Planning and Flexible Arm Movement Control

This talk will survey recent results concerning how the brain self-organizes its planning and control of flexible arm movements to accomplish spatially defined tasks at variable speeds and forces with a redundant arm that may be confronted with obstacles. Recent work from our group on this topic includes the following four themes. Theme 1 concerns the design of a neural controller for generating handwriting at variable speeds, sizes, and styles. This model, called VITEWRITE, was first described in Bullock, Grossberg, & Mannes (1993). The VITEWRITE model addresses a number of key issues concerning the skilled performance of sequential actions: What is a motor program? How can a complex movement be flexibly performed at will with variable speed, size, and style without requiring new learning? How does the brain control a redundant manipulator that possesses more degrees of freedom than the space in which it moves? How can smooth curvilinear movements be organized by such a redundant manipulator? In particular, how is the timed launching of different groups, or synergies, of muscles achieved so that the desired directions, distances, and curvatures of movement are achieved? How, moreover, can "acts of will" that vary the speed and size of movements change distances and curvatures of movement without disrupting the correct directions of movement that preserve its overall form through time? A schematic of the model is provided in Figure 1. The VITEWRITE model introduces a new concept of how a "motor program" can control skilled sequential movements. This concept introduces a solution of the problems of how writing can preserve its form when acts of will change its size and speed, and of how acts of will can change writing style by perturbing an invariant representation of the program. This motor program is not explicitly represented in the model. Rather, it is an emergent property of feedback interactions between a working memory representation of desired movement directions (called a Vector Plan), and a trajectory generator for moving the limb (called a VITE circuit). Simple volitional commands for changing the speed (GO) or size (GRO) of writing have the desired effect when they are input to these processes at the correct processing stages (see Figure 1). The VITEWRITE model also shows how the use of a redundant manipulator can simplify the problem of motor planning with these invariant properties by enabling the velocity profiles that control individual motor synergies to be unimodal. A simple rule for reading out new synergy commands exploits this unimodal form. During a movement, when the velocity profile of one synergy reaches its (unimodal) maximum, a new synergy’s planning vector can be read out. This rule preserves written form in response to variable GO-controlled speeds. The VITEWRITE model exhibits a number of properties of human handwriting. For example, the "two-thirds power law" of Lacquaniti et al. (1983), which says that angular velocity is the two-thirds power of curvature, is an emergent property of network interactions. In summary, the V/TEWRITE model demonstrates how a working memory can control writing movements that exhibit many properties of human handwriting when it interacts reciprocally with a suitably defined trajectory generator coupled to a model hand with redundant degrees of freedom. These results extend the applicability of the earlier VITE model (Bullock & Grossberg 1988) from the control of reaching behaviors to the control of complex curvilinear trajectories.