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Abstract: Based on existing data, we wish to put forward a biological model of motor system on the neuron scale. Then we indicate its implications in statistics and learning. Specifically, neuron’s firing frequency and synaptic strength are probability estimates in essence. And the lateral inhibition also has statistical implications. From the standpoint of learning, dendritic competition through retrograde messengers is the foundation of conditional reflex and “grandmother cell ” coding. And they are the kernel mechanisms of motor learning and sensory-motor integration respectively. Finally, we compare motor system with sensory system. In short, we would like to bridge the gap between molecule evidences and computational models. Main Text: Great strides have been made in the research of motor learning (1-5). Until now however, there still exists a gap between existing models and physiological data (6-8). Control models such as the internal forward models (1, 2) and optimal control models (3) are mainly on the module scale. Learning models such as CMAC are difficult for biological implementation (4, 5). Based on existing data and theories, we wish to put forward a quantitative motor learning model on the neuron scale. Inspired by the “self-organization ” idea (9), we only make local rules about neuron and synapse, and the neural network will emerge automatically. Moreover, both excitory and inhibitory neurons share the same framework, merely different in details. Motor neurons in this paper include those in the cerebellum, DCN (deep cerebellar nuclei) and basal ganglia, but excluding Pyramid cells in the cerebral motor area. All ci in this paper are constants, and they have different meanings in different paragraphs. Information about sensory memory can be found in our previous work (10). Motor neuron model is as follows (N): N1)