Previously, I wrote that motor learning is a trial and error process which consists of making random motor output connections and testing them for fitness. There are two fitness criteria used in motor learning. First, a motor connection must not achieve goals other than the one it is associated with. Second, a motor connection must not cause conflicts with other connections on the same motor neuron. In this post, I explain what a motor conflict is within the context of motor control.
Motor control is a process whereby the brain's cortex sends motor commands to motor neurons in order to effect goal-directed, coordinated behavior. Motor commands are discrete signals of which there are two types, start and stop. These correspond roughly to the excitatory and inhibitory signals that are observed in the motor systems of humans and animals. A start command starts an action (such as contracting a muscle) while a stop command stops an action already in progress.
An important question is, how does the motor system control the amount of force exerted by the muscles? The answer is that every motor action is serviced by a set of motor neurons and each neuron in the set is pre-wired to exert a different force magnitude. It is up to the motor learning system to determine which ones to activate for a given goal or situation.
Since motor neurons are shared by a large number of cortical programs, it is important that the command signals are not in conflict. There are two principles that govern motor coordination.
1. A motor neuron must not receive more than one command at a time.
2. No action can be started if it is already started or stopped if it is already stopped.
During motor learning, motor connections are monitored to see if they violate the coordination rules. Any violation results in the weakening of the conflicting connections. In the end, only the strongest connections survive.
The principles of goal-directed motor learning are simple, powerful and can be easily implemented in a computer. However, they are of no use unless motor signals can be generated in an orderly, predictable and coherent manner. In other words, an intelligent system must have a sophisticated perceptual mechanism before it can even begin to behave sensibly to achieve its goals.
The topic of goals brings us to the notion of motivation. How can an intelligent system have goals unless it is somehow motivated to pursue those goals. Motivation is a huge part of intelligence. In fact, there can be no real intelligence without it. But how can a machine have likes and dislikes? In an upcoming article, I will dig deep into this topic and the importance of motivation to adaptation and survival.
The Holy Grail of Robotics
Goal Oriented Motor Learning
Raiders of the Holy Grail
Secrets of the Holy Grail