Motor Learning Mechanism on the Neuron Scale

TL;DR

This paper proposes a biological model of the motor system at the neuron scale, linking neuron firing, synaptic strength, and lateral inhibition to statistical principles, and explores their roles in motor learning and sensory integration.

Contribution

It introduces a novel neuron-scale biological model of motor learning, connecting molecular evidence with computational mechanisms.

Findings

Neuron firing frequency and synaptic strength as probability estimates

Lateral inhibition has statistical implications

Dendritic competition underpins conditional reflex and sensory motor integration

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 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.