  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 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.
