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The Neuroscience of Rhythm refers to the various forms of rhythm generated by the CNS. Rhythm is defined as a “movement marked by the regulated succession of strong and weak elements, or of opposite or different conditions.” Neurons in the human brain are capable of firing in specific patterns which cause oscillations. The brain possesses many different types of oscillators with different periods. In fact, oscillators are simultaneously outputting frequencies from .02 Hz to 600 Hz. This may seem over complicated. It is now well known that a computer is capable of running thousands of processes with just one high frequency clock. So why do humans have so many? The answer is evolution. Prior organisms had no need for a fast responding oscillator. The bottom line is we now have many clocks that permit quick response to constantly changing sensory input while still maintaining the autonomic processes that keep us alive. This method modulates and controls a great deal of bodily functions.

Autonomic Rhythms
The autonomic nervous system is responsible for many of the regulatory processes that sustain human life. Autonomic regulation is involuntary, meaning we do not have to think about it for it to take placee. A great deal of these are dependent upon a certain rhythm, such as sleep, heart rate, and breathing.

Circadian Rhythms
Circadian literally translates to “about a day” in Latin. This refers to the human 24 hour cycle of sleep and wakefulness. This cycle is driven by light. The human body must photoentrain itself in order to make this happen. Although the rods are responsible for sensing light, they are not what sets the biological clock. The photosensitive retinal ganglion cells contain a pigment called melanopsin. This photopigment is depolarized in the presence of light, unlike the rods and cones which are hyperpolarized. Melanopsin encodes the day-night cycle to the suprachiasmatic nucleus (SCN) via the retinohypothalamic tract. The SCN evokes a response from the spinal cord. These preganglionic neurons in the spinal cord modulate the superior cervical ganglia, which synapses on the pineal gland. The pineal gland synthesizes the neurohormone melatonin from tryptophan. Melatonin is secreted into the bloodstream where it effects neural activity by interacting with melatonin receptors on the SCN. The SCN is then able to influence the sleep wake cycle, acting as the “apex of a hierarchy” that governs physiological timing functions. . "Rest and sleep are the best example of self-organized operations within neuronal circuits".

Sleep and memory have been closely correlated for over a century. It seemed logical that the rehearsal of learned information during the day, such as in dreams, could be responsible for this consolidation. REM sleep was first studied in 1953. It was thought to be the sole contributor to memory due to its association with dreams. It has recently been suggested that if sleep and waking experience are found to be using the same neuronal content, it is reasonable to say that all sleep has a role in memory consolidation. This is supported by the rhythmic behavior of the brain. Harmonic oscillators have the capability to reproduce a perturbation that happened in previous cycles. It follows that when the brain is unperturbed, such as during sleep, it is in essence rehearsing the perturbations of the day. Recent studies have confirmed that off wave states, such as slow-sleep, play a part in consolidation as well as REM sleep. There have even been studies done implying that sleep can lead to insight or creativity. Jan Born, from the University of Lubeck, showed subjects a number series with a hidden rule. She allowed one group to sleep for three hours, while the other group stayed awake. While the awake group showed no progress most of the group that was allowed to sleep were able to solve the rule. This is just one example of how rhythm could contribute to humans unique cognitive abilities. .

Central Pattern Generation (CPG)
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Cognition
This may be the hardest aspect of rhythm to understand. This refers to the types of rhythm that we are able to generate, be it from recognition of others, as in avian song learning, or sheer creativity.

Sports
The muscle coordination, muscle memory, and innate game awareness all rely on our nervous system to produce a specific firing pattern in response to a either an efferent or afferent signal.

Music
The ability to perceive and generate music is frequently studied ("Brain beats", "Beat this"). This is rhythm in its most obvious form.

Computational Models
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. Many computational models have attempted to quantify the process of how various rhythms are created by humans.

Avian Song Learning
Juvenile avian song learning is one of the best animal models used to study generation and recognition of rhythm. The ability for birds to process a tutor song and then generate a perfect replica of that song, underlies our ability to learn rhythm.

Two very famous computational neuroscientists Kenji Doya and Terrence J. Sejnowski created a model of this using the zebra finch as target organism. The zebra finch is perhaps one of the most easily understood examples of this among birds. The young zebra finch is exposed to a “tutor song” from the adult, during a critical period. This is defined as the time of life that learning can take place, in other words when the brain has the most plasticity. After this period, the bird is able to produce an adult song, which is said to be crystallized at this point. Doya and Sejnowski evaluated three possible ways that this leaning could happen. The first was an immediate, one shot perfection of the tutor song. While this is a very attractive model for certain species such as humans, it was unlikely that this took place in birds because the learning process takes a long time. The second scheme was that of error learning. This refers to a signal generated by the avian brain that corresponds to the error between the tutor song and newly generated template song. This is a very reasonable explanation, so reasonable in fact that it is the adopted theory of current neuroscientists such as Sam Sober of Emory University. Unfortunately Doya and Sejnowski found too many issues with most of the models based on this at the time, which led them to stray away from this explanation. They settled on the third scheme which employs reinforcement learning to explain the process. 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. In the case of the zebra finch, this articulator is the robust nucleus of archistriatum or RA. The NMDA receptors allow the RA to be more likely to produce this template of the tutor song, thus leading to leaning of the correct song.

Dr. Sam Sober explains the process of tutor song recognition and generation using error learning. This refers to a signal generated by the avian brain that corresponds to the error between the tutor song and newly generated template song. The signal is simply optimized in order to be as small as possible, which results in the learning of the song.

Macaque Motor Cortex
This animal model has been said to be more similar to humans than birds.

Current Methods
At the moment, recording methods are not capable of simulataneously measurering small and large areas at the same time, with the temporal resolution that the circuitry of the brain requires. These techniques include EEG, MEG, fMRI, optical recordings, and single-cell recordings.

Future
Techniques such as large scale single-cell recordings are move in the direction of analyze overall brain rhythms. However, these require invasive procedures, such as tetrode implantation, which does not allow a healthy brain to be studied. Also, pharmacological manipulation, cell culture imaging and computational biology all make attempts at doing this but in the end they are indirect.