Nerve cells, also known as neurons in the human brain are capable of firing in specific patterns which cause oscillations.
This multi-clock system permits quick response to constantly changing sensory input while still maintaining the autonomic processes that sustain life.
Preganglionic neurons in the spinal cord modulate the superior cervical ganglia, which synapses on the pineal gland.
The SCN is then able to influence the sleep wake cycle, acting as the "apex of a hierarchy" that governs physiological timing functions.
Furthermore, new motor patterns, such as athletic skills or the ability to play an instrument, also use half-center oscillators and are simply learned perturbations to CPG's already in place.
They are capable of maintaining a sustained level of oxygen in the blood by triggering the lungs to expand and contract at the correct time.
For example, in basketball, in order to anticipate the game one must recognize rhythmic patterns of other players and perform actions calibrated to these movements.
[6] Although the exact oscillatory pattern that modulates different sports has not been found, there have been studies done to show a correlation between athletic performance and circadian timing.
[7][8] The ability to perceive and generate music is frequently studied as a way to further understand human rhythmic processing.
Human beings have an innate ability to listen to a rhythm and track the beat, as seen here "Dueling Banjos".
This would mean that those areas of the brain would be responsible for spontaneous rhythm generation, although further research is required to prove this.
[11] Computational neuroscience is the theoretical study of the brain used to uncover the principles and mechanisms that guide the development, organization, information-processing and mental abilities of the nervous system.
[12] Juvenile avian song learning is one of the best animal models used to study generation and recognition of rhythm.
Two very famous computational neuroscientists Kenji Doya and Terrence J. Sejnowski created a model of this using the Zebra Finch as target organism.
Reinforcement learning consists of a "critic" in the brain capable of evaluating the difference between the tutor and the template song.
Assuming the two are closer than the last trial, this "critic" then sends a signal activating NMDA receptors on the articulator of the song.
This refers to a signal generated by the avian brain that corresponds to the error between the tutor song and the auditory feedback the bird gets.
Although it's clear that humans are constantly adjusting their speech while birds are believed to have crystallized their song upon reaching adulthood.
It has been shown that humans demonstrate 15–30 Hz (Beta) oscillations in the cortex while performing muscle coordination exercises.
The cortical local field potentials (LFPs) of conscious monkeys were recorded while they performed a precision grip task.
One example of how this model is used is the investigation of the role of motor cortex PTNs in "corticomuscular coherence" (muscle coordination).
In similar study where LFPs were recorded from macaque monkeys while they performed a precision grip task, it was seen that the disruption of the PTN resulted in a greatly reduced oscillatory response.
[19] At the moment, recording methods are not capable of simultaneously measuring small and large areas at the same time, with the temporal resolution that the circuitry of the brain requires.
Also, pharmacological manipulation, cell culture imaging and computational biology all make attempts at doing this but in the end they are indirect.
[1] The classification of frequency borders allowed for a meaningful taxonomy capable of describing brain rhythms, known as neural oscillations.