title: Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator.
abstract: The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.
authors.affiliation: Laboratory of Neurophysiology and Movement Biomechanics, CP601, ULB Neuroscience Institute, Université Libre de Bruxelles Brussels, Belgium.
authors.forename: Thomas
authors.initials: T
authors.lastname: Hoellinger
authors.forename: Mathieu
authors.initials: M
authors.lastname: Petieau
authors.forename: Matthieu
authors.initials: M
authors.lastname: Duvinage
authors.forename: Thierry
authors.initials: T
authors.lastname: Castermans
authors.forename: Karthik
authors.initials: K
authors.lastname: Seetharaman
authors.forename: Ana-Maria
authors.initials: AM
authors.lastname: Cebolla
authors.forename: Ana
authors.initials: A
authors.lastname: Bengoetxea
authors.forename: Yuri
authors.initials: Y
authors.lastname: Ivanenko
authors.forename: Bernard
authors.initials: B
authors.lastname: Dan
authors.forename: Guy
authors.initials: G
authors.lastname: Cheron
keywords: biological oscillations; central pattern generator (CPG); dynamical recurrent neural network (DRNN); human locomotion; kinematics; neurophysiology of walking