The emulation theory of representation postulates that there are multiple internal modeling circuitries in the brain referred to as emulators. These emulators mimic the input-output patterns of many cognitive operations including action, perception, and imagery. Often running in parallel, these emulators provide resultant feedback in the form of mock sensory signals of a motor command with less delay than sensors. These forward models receive efference copies of input motor commands being sent to the body and the resulting output sensory signals. Emulators are continually updating so as to give the most accurate anticipatory signal following motor inputs.
Mechanics and structure
Little is known about the overall structure of emulators. It could operate like a search glossary with a very large associative memory of input-output sequences. Under this system, the emulator receives a motor command input, finds the closest matching input from its database, and then sends the associated output in that sequence. The other model is an articulated emulator. This model requires that for each significant sensor of the human musculoskeletal system there is a group of neurons with a parallel firing frequency within the emulator. These groups of neurons would receive the same input as that being sent to their corresponding part of the musculoskeletal system. For example, when raising one's hand signals will be sent to neurons responsible for wrist, elbow, and shoulder angles and arm angular inertia.
Regardless of this structure both systems will grow and change over time. This is due to constant, fluctuating noise from the environment and the fact that the body changes over time. Growing limbs and muscles result in changes in both required input commands and the resulting output. This requires a degree of plasticity in the emulators. Emulators are thus continually updating, always receiving the resulting output from the musculoskeletal system from an inputted command and comparing it to its own output. It is likely that this is accomplished through Kalman Filter. Complete correction from the difference in outputs, however, is not applied to the emulators.
Noise is a constant, fluctuating variable affecting musculoskeletal sensory output. Thus, output differences are almost always greater than they actually should be. Consequently, an internal calculation is made; taking into account the relative reliability of the emulator's output and if the situational conditions tend to result in very variable or stable sensory outputs, the correction is weighted and applied.
The location of these emulators have been found to be largely dependent on the motor system they provide processing and information for.
Cognitive function
Emulators are helpful tools to the body. Sensory feedback naturally takes longer than a predetermined forward model that the brain can compute or access directly. This timing gap can be delayed further by other environmental or physical factors that often occur throughout life. Emulators allow a mock output signal that is often close to that of the real thing. It is further postulated that the emulator can be run independently thus allowing the brain see the likely outcome of a command without running it on the entire system. Furthermore, when returning sensory information has noise and\or holes, the emulator can be run in parallel, making corrections and filling in gaps.
Motor control
Experiments have found that the temporal length of the motor control loop takes approximately 250-400 ms. Motor centers, however, are already making corrections to their previous motor plan about 70 ms after the onset of the movement based on peripheral information that should not have arrived yet. It is thus likely that there are motor emulators that receive and process efferent copies and then send predictive peripheral output before the actual peripheral output from sensors is received. Motor centers often have to run very quickly. Feedback delays and peripheral errors make real-time, rapid motor movement very difficult and liable to failure. Through the use of emulators motor centers can receive fast and reliable feedback in the form of a priori predictions. The later real periphery output can then be incorporated into a more accurate model.