Motor Learning Mechanism on the Neuron Scale
This work aims to bridge the gap between molecular evidence and computational models in neuroscience, offering a theoretical framework for understanding motor learning mechanisms.
The paper proposes a biological model of the motor system at the neuron scale, interpreting neuron firing frequency and synaptic strength as probability estimates and linking dendritic competition to mechanisms like conditional reflex and grandmother cell coding for motor learning and sensory-motor integration.
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.