Implications of a neural network model of early sensori-motor development for the field of developmental neurology.

This paper reports on a neural network model for early sensori-motor development and on the possible implications of this research for our understanding and, eventually, treatment of motor disorders like cerebral palsy. We recapitulate the results we published in detail in a series of papers [1-4]. The neural circuits in the model self-organize on the basis of rhythmic activity spontaneously generated in the model. This indicates the importance of endogenously generated activity in the developing brain. We also show that afferent feed-back from the mechanical part of the model is easily incorporated in the neural part of the model. In this way the model acquires reflex-related properties which have long been demonstrated in man. In the discussion we relate these experimental findings to the variability concept from developmental neurology and show how variable motor performance is important for motor learning. We also discuss possible implications of our modelling effort for movement disorders, specifically spastic cerebral palsy.