Neural control of interlimb oscillations. I. Human bimanual coordination.

How do humans and other animals accomplish coordinated movements? How are novel combinations of limb joints rapidly assembled into new behavioral units that move together in in-phase or anti-phase movement patterns during complex movement tasks? A neural central pattern generator (CPG) model simulates data from human biman- ual coordination tasks. As in the data, anti-phase oscillations at low frequencies switch to in-phase oscillations at high fre- quencies, in-phase oscillations occur at both low and high frequencies, phase fluctuations occur at the anti-phase in- phase transition, a "seagull effect" of larger errors occurs at intermediate phases, and oscillations slip toward in-phase and anti-phase when driven at intermediate phases. These oscillations and bifurcations are emergent properties of the CPG model in response to volitional inputs. The CPG model is a version of the Ellias-Grossberg oscillator. Its neurons obey Hodgkin-Huxley type equations whose excitatory sig- nals operate on a faster time scale than their inhibitory sig- nals in a recurrent on-center off-surround anatomy. When an equal command or GO signal activates both model chan- nels, the model CPG can generate both in-phase and anti- phase oscillations at different GO amplitudes. Phase transi- tions from either in-phase to anti-phase oscillations, or from anti-phase to in-phase oscillations, can occur in different pa- rameter ranges, as the GO signal increases. 1 In-phase and anti-phase bimanual coordination Humans and other animals effortlessly control their limbs to accomplish coordinated movements. In particular, novel combinations of joints can be rapidly assembled into new behavioral units, or synergies, that are capable of moving to- gether in in-phase or anti-phase movement patterns to carry out complex movement tasks like tool use, dancing, piano playing, and the like. In order to study this competence, an experimental paradigm was previously developed in which humans were asked to move fingers from both hands at variable frequencies and to do so in in-phase or anti-phase rhythms. Data from these experiments exhibit characteristic properties which provide clues to how new combinations of joints can be rapidly bound together to generate coordinated movement patterns. This article describes a neural network model that sug- gests how novel joint combinations can be rapidly bound together in rhythmic patterns. These patterns are emergent properties due to network interactions. They are not explic- itly represented or programmed in the network. The model simulates parametric properties of human movement data as emergent, or interactive, properties of nonlinear network in- teractions. This network takes the form of a central pattern generator (CP) that coordinates the movement across limb joints when volitional input signals perturb the network. For example, in a bimanual finger tapping task, Yaman- ishi et al. (1980) required subjects to tap keys in time to visual cues. The timing of the cues was varied across ten relative phases: (0.0,0.1,0.2,... 1.0), where 0.0 = 0° and 1.0 = 360°. The authors observed two properties in the re- sponses of their subjects. First, the subjects' fingers tended to slip from intermediate relative phase relationships toward purely in-phase (0.0 and 1.0) or anti-phase (0.5) relation- ships. Second, the observed in-phase and anti-phase oscil- lations exhibited less variability than intermediate phase re- lationships. That is, when the subjects were asked to syn- chronize to signals whose phase relationships varied from 0.0 to 1.0, the standard deviation of the errors was lowest when the phase relationship was near in-phase (0.0 and 1.0) or pure anti-phase (0.5). The standard deviation of the errors increased as the subjects were required to move away from the in-phase or pure anti-phase oscillations. These two prop-~