Neuroscience of rhythm
The neuroscience of rhythm refers to the various forms of rhythm generated by the
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 place. 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 or synchronize itself with light in order to make this happen. The rod cells are the
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.
Central pattern generation
A central pattern generator (CPG) is defined as a neural network that does not require sensory input to generate a rhythm. This rhythm can be used to regulate essential physiological processes. These networks are often found in the spinal cord. It has been hypothesized that certain CPG's are hardwired from birth. For example, an infant does not have to learn how to breathe and yet it is a complicated action that involves a coordinated rhythm from the medulla. The first CPG was discovered by removing neurons from a locust. It was observed that the group of neurons was still firing as if the locust was in flight.[3] In 1994, evidence of CPG's in humans was found. A former quadrapalegic began to have some very limited movement in his lower legs. Upon lying down, he noticed that if he moved his hips just right his legs began making walking motions. The rhythmic motor patterns were enough to give the man painful muscle fatigue.[4]
A key part of CPG's is half-center oscillators. In its simplest form, this refers to two neurons capable of rhythmogenesis when firing together. The generation of a biological rhythm, or rhythmogenesis, is done by a series of inhibition and activation. For example, a first neuron inhibits a second one while it fires, however, it also induces slow depolarization in the second neuron. This is followed by the release of an action potential from the second neuron as a result of depolarization, which acts on the first in a similar fashion. This allows for self-sustaining patterns of oscillation. 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.[3]
Respiration
Ventilation requires periodic movements of the respiratory muscles. These muscles are controlled by a rhythm generating network in the brain stem. These neurons comprise the
Cognition
This refers to the types of rhythm that humans are able to generate, be it from recognition of others or sheer creativity.
Sports
Muscle coordination, muscle memory, and innate game awareness all rely on the nervous system to produce a specific firing pattern in response to an either an efferent or afferent signal. Sports are governed by the same production and perception of oscillations that govern much of human activity. 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. "The rhythm of a game of basketball emerges from the rhythm of individuals, the rhythm among team members, and the rhythmic contrasts between opposing teams".[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. It has been shown certain times of the day are better for training and gametime performance. Training has the best results when done in the morning, while it is better to play a game at night.[7][8]
Music
The ability to perceive and generate music is frequently studied as a way to further understand human rhythmic processing. Research projects, such as Brain Beats,[9] are currently studying this by developing beat tracking algorithms and designing experimental protocols to analyze human rhythmic processing. This is rhythm in its most obvious form. Human beings have an innate ability to listen to a rhythm and track the beat, as seen here "Dueling Banjos".[10] This can be done by bobbing the head, tapping of the feet or even clapping. Jessica Grahn and Matthew Brett call this spontaneous movement "motor prediction". They hypothesized that it is caused by the basal ganglia and the supplementary motor area (SMA). This would mean that those areas of the brain would be responsible for spontaneous rhythm generation, although further research is required to prove this. However, they did prove that the basal ganglia and SMA are highly involved in rhythm perception. In a study where patients brain activity was recorded using fMRI, increased activity was seen in these areas both in patients moving spontaneously (bobbing their head) and in those who were told to stay still.[11]
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.[12]
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
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 the auditory feedback the bird gets. The signal is simply optimized in order to be as small of a difference as possible, which results in the learning of the song. Dr. Sober believes that this is also the mechanism employed in human speech learning. Although it's clear that humans are constantly adjusting their speech while birds are believed to have crystallized their song upon reaching adulthood. He tested this idea by using headphones to alter a Bengalese finch's auditory feedback. The bird actually corrected for up to 40% of the perturbation. This provides strong support for error learning in humans.[14]
Macaque motor cortex
This animal model has been said to be more similar to humans than birds. It has been shown that humans demonstrate 15–30 Hz (Beta) oscillations in the cortex while performing muscle coordination exercises.
Imaging
Current methods
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. These techniques include
Future
Techniques such as large scale single-cell recordings are movements in the direction of analyzing 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.[1]
Frequency bands
The classification of frequency borders allowed for a meaningful taxonomy capable of describing brain rhythms, known as neural oscillations.
Class | Range |
---|---|
Delta | .5–4 Hz[1] |
Theta | 4–8 Hz[1] |
Alpha | 8–12 Hz[1] |
Beta | 12–30 Hz[1] |
Gamma | >30 Hz[1] |
References
- ^ a b c d e f g h i j Buzsáki, G (2006). The Rhythms of the Brain. Oxford Press.
- ^ Purves, Dale (2012). Neuroscience. Vol. V. Sinauer Associates, INC. pp. 628–636.
- ^ ISBN 978-0-470-01590-2.
- PMID 7953595.
- ISBN 978-3-642-64619-5.
- ^ Handel, Stephen (1989). Listening: An introduction to the perception of auditory events. MIT Press.
- PMID 9381059.)
{{cite journal}}
: CS1 maint: multiple names: authors list (link - S2CID 2935066.
- ^ Brain Beats
- ^ "Dueling Banjos"
- S2CID 5992236.
- ^ Trappenberg, Thomas P (2002). The Fundamentals of Computational Neurosciences. Oxford Press.
- ^ Doya, Kenji & Terrence J. Sejnowski (1999). The New Cognitive Neurosciences. Vol. II. MIT Press. pp. 469–482.
- PMID 19525945.
- PMID 8788955.
- PMID 9212286.
- S2CID 12959403.
- PMID 9175005.
- PMID 10087353.