A Model for Self-Organization of Sensorimotor Function: The Spinal Monosynaptic Loop

Recent spinal cord literature abounds with descriptions of genetic preprogramming and the molecular control of circuit formation. In this paper we explore to what extent circuit formation based on learning rather than preprogramming could explain some prominent aspects of the spinal cord connectivity patterns observed in animals. To test this we developed an artificial organism with a basic musculoskeletal system and proprioceptive sensors, connected to a neural network. We adjusted the initially randomized gains in the neural network according to a Hebbian plasticity rule while exercising the model system with spontaneous muscle activity patterns similar to those observed during early fetal development. The resulting connection matrices support functional self-organization of the mature pattern of Ia to motoneuron connectivity in the spinal circuitry. More coordinated muscle activity patterns such as observed later during neonatal locomotion impaired projection selectivity. These findings imply a generic functionality of a musculoskeletal system to imprint important aspects of its mechanical dynamics onto a neural network, without specific preprogramming other than setting a critical period for the formation and maturation of this general pattern of connectivity. Such functionality would facilitate the successful evolution of new species with altered musculoskeletal anatomy and it may help to explain patterns of connectivity and associated reflexes that appear during abnormal development.