Simulating Human Single Motor Units Using Self-Organizing Agents

Understanding functional synaptic connectivity of human central nervous system is one of the holy grails of the neuroscience. Due to the complexity of nervous system, it is common to reduce the problem to smaller networks such as motor unit pathways. In this sense, we designed and developed a simulation model that learns acting in the same way of human single motor units by using findings on human subjects. The developed model is based on self-organizing agents whose nominal and cooperative behaviors are based on the current knowledge on biological neural networks. The results show that the simulation model generates similar functionality with the observed data.