Neural oscillations, orbrainwaves, are rhythmic or repetitive patterns of neural activity in thecentral nervous system.Neural tissue can generateoscillatory activity in many ways, driven either by mechanisms within individualneurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations inmembrane potential or as rhythmic patterns ofaction potentials, which then produce oscillatory activation ofpost-synaptic neurons. At the level ofneural ensembles, synchronized activity of large numbers of neurons can give rise tomacroscopic oscillations, which can be observed in anelectroencephalogram. Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations isalpha activity.
Neural oscillations in humans were observed by researchers as early as 1924 (byHans Berger). More than 50 years later, intrinsic oscillatory behavior was encountered in vertebrate neurons, but its functional role is still not fully understood.[3] The possible roles of neural oscillations includefeature binding,information transfer mechanisms and thegeneration of rhythmic motor output. Over the last decades more insight has been gained, especially with advances inbrain imaging. A major area of research inneuroscience involves determining how oscillations are generated and what their roles are. Oscillatory activity in the brain is widely observed at differentlevels of organization and is thought to play a key role in processing neural information. Numerous experimental studies support a functional role of neural oscillations; a unified interpretation, however, is still lacking.
Richard Caton discovered electrical activity in the cerebral hemispheres of rabbits and monkeys and presented his findings in 1875.[4]Adolf Beck published in 1890 his observations of spontaneous electrical activity of the brain of rabbits and dogs that included rhythmic oscillations altered by light, detected with electrodes directly placed on the surface of the brain.[5] BeforeHans Berger,Vladimir Vladimirovich Pravdich-Neminsky published the first animal EEG and theevoked potential of a dog.[6]
Neural oscillations are observed throughout the central nervous system at all levels, and includespike trains,local field potentials and large-scaleoscillations which can be measured byelectroencephalography (EEG). In general, oscillations can be characterized by theirfrequency,amplitude andphase. These signal properties can be extracted from neural recordings usingtime-frequency analysis. In large-scale oscillations, amplitude changes are considered to result from changes in synchronization within aneural ensemble, also referred to as local synchronization. In addition to local synchronization, oscillatory activity of distant neural structures (single neurons or neural ensembles) can synchronize. Neural oscillations and synchronization have been linked to many cognitive functions such as information transfer, perception, motor control and memory.[7][8][9][10]
The opposite of neuron synchronization is neural isolation, which is when electrical activity of neurons is not temporally synchronized. This is when the likelihood of the neuron to reach its threshold potential for the signal to propagate to the next neuron decreases. This phenomenon is typically observed as the spectral intensity decreases from the summation of these neurons firing, which can be utilized to differentiate cognitive function or neural isolation. However, new non-linear methods have been used that couple temporal and spectral entropic relationships simultaneously to characterize how neurons are isolated, (the signal's inability to propagate to adjacent neurons), an indicator of impairment (e.g., hypoxia).[1]
Neural oscillations have been most widely studied in neural activity generated by large groups of neurons. Large-scale activity can be measured by techniques such as EEG. In general, EEG signals have a broad spectral content similar topink noise, but also reveal oscillatory activity in specific frequency bands. The first discovered and best-known frequency band isalpha activity (8–12Hz)[11][12][13] that can be detected from theoccipital lobe during relaxed wakefulness and which increases when the eyes are closed.[14] Other frequency bands are:delta (1–4 Hz),theta (4–8 Hz),beta (13–30 Hz), lowgamma (30–70 Hz),[15] and high gamma (70–150 Hz) frequency bands. Faster rhythms such as gamma activity have been linked to cognitive processing. Indeed, EEG signals change dramatically during sleep. In fact, different sleep stages are commonly characterized by their spectral content.[16] Consequently, neural oscillations have been linked to cognitive states, such asawareness andconsciousness.[17][18][15][13]
Although neural oscillations in human brain activity are mostly investigated using EEG recordings, they are also observed using more invasive recording techniques such assingle-unit recordings. Neurons can generate rhythmic patterns ofaction potentials or spikes. Some types of neurons have the tendency to fire at particular frequencies, either asresonators[19] or asintrinsic oscillators.[2]Bursting is another form of rhythmic spiking. Spiking patterns are considered fundamental forinformation coding in the brain. Oscillatory activity can also be observed in the form ofsubthreshold membrane potential oscillations (i.e. in the absence of action potentials).[20] If numerous neurons spike insynchrony, they can give rise to oscillations inlocal field potentials. Quantitative models can estimate the strength of neural oscillations in recorded data.[21]
Neural oscillations are commonly studied within a mathematical framework and belong to the field ofneurodynamics, an area of research in thecognitive sciences that places a strong focus on the dynamic character of neural activity in describingbrain function.[22] It considers the brain adynamical system and usesdifferential equations to describe how neural activity evolves over time. In particular, it aims to relate dynamic patterns of brain activity to cognitive functions such as perception and memory. In very abstract form, neural oscillations can be analyzedanalytically.[23][24] When studied in a more physiologically realistic setting, oscillatory activity is generally studied usingcomputer simulations of acomputational model.
The functions of neural oscillations are wide-ranging and vary for different types of oscillatory activity. Examples are the generation of rhythmic activity such as aheartbeat and theneural binding of sensory features in perception, such as the shape and color of an object. Neural oscillations also play an important role in manyneurological disorders, such as excessive synchronization duringseizure activity inepilepsy, ortremor in patients withParkinson's disease. Oscillatory activity can also be used to control external devices such as abrain–computer interface.[25]
Oscillatory activity is observed throughout thecentral nervous system at all levels of organization. Three different levels have been widely recognized: the micro-scale (activity of a single neuron), the meso-scale (activity of a local group of neurons) and the macro-scale (activity of different brain regions).[26]
Neurons generateaction potentials resulting from changes in the electric membrane potential. Neurons can generate multiple action potentials in sequence forming so-called spike trains. These spike trains are the basis forneural coding and information transfer in the brain. Spike trains can form all kinds of patterns, such as rhythmic spiking andbursting, and often display oscillatory activity.[27] Oscillatory activity in single neurons can also be observed insub-threshold fluctuations in membrane potential. These rhythmic changes in membrane potential do not reach the critical threshold and therefore do not result in an action potential. They can result from postsynaptic potentials from synchronous inputs or from intrinsic properties of neurons.
Neuronal spiking can be classified by its activity pattern. The excitability of neurons can be subdivided in Class I and II. Class I neurons can generate action potentials with arbitrarily low frequency depending on the input strength, whereas Class II neurons generate action potentials in a certain frequency band, which is relatively insensitive to changes in input strength.[19] Class II neurons are also more prone to display sub-threshold oscillations in membrane potential.
A group of neurons can also generate oscillatory activity. Through synaptic interactions, thefiring patterns of different neurons may become synchronized and the rhythmic changes in electric potential caused by their action potentials may accumulate (constructive interference). That is, synchronized firing patterns result in synchronized input into other cortical areas, which gives rise to large-amplitude oscillations of thelocal field potential. These large-scale oscillations can also be measured outside the scalp usingelectroencephalography (EEG) andmagnetoencephalography (MEG). The electric potentials generated by single neurons are far too small to be picked up outside the scalp, and EEG or MEG activity always reflects the summation of the synchronous activity of thousands or millions of neurons that have similar spatial orientation.[28]
Neurons in aneural ensemble rarely all fire at exactly the same moment, i.e. fully synchronized. Instead, the probability of firing is rhythmically modulated such that neurons are more likely to fire at the same time, which gives rise to oscillations in their mean activity. (See figure at top of page.) As such, the frequency oflarge-scale oscillations does not need to match the firing pattern of individual neurons. Isolated cortical neurons fire regularly under certain conditions, but in the intact brain, cortical cells are bombarded by highly fluctuating synaptic inputs and typically fire seemingly at random. However, if the probability of a large group of neurons firing is rhythmically modulated at a common frequency, they will generate oscillations in the mean field. (See also figure at top of page.)[27]
Neural ensembles can generate oscillatory activityendogenously through local interactions between excitatory and inhibitory neurons. In particular, inhibitoryinterneurons play an important role in producing neural ensemble synchrony by generating a narrow window for effective excitation and rhythmically modulating the firing rate of excitatory neurons.[29]
Neural oscillation can also arise from interactions between different brain areas coupled through the structuralconnectome.Time delays play an important role here. Because all brain areas are bidirectionally coupled, these connections between brain areas formfeedback loops.Positive feedback loops tend to cause oscillatory activity where frequency is inversely related to the delay time. An example of such a feedback loop is the connections between thethalamus andcortex – thethalamocortical radiations. This thalamocortical network is able to generate oscillatory activity known asrecurrent thalamo-cortical resonance.[30] The thalamocortical network plays an important role in the generation ofalpha activity.[31][32] In a whole-brain network model with realistic anatomical connectivity and propagation delays between brain areas, oscillations in thebeta frequency range emerge from the partial synchronisation of subsets of brain areas oscillating in the gamma-band (generated at the mesoscopic level).[33]
Scientists have identified some intrinsicneuronal properties that play an important role in generating membrane potential oscillations. In particular,voltage-gated ion channels are critical in the generation of action potentials. The dynamics of these ion channels have been captured in the well-establishedHodgkin–Huxley model that describes how action potentials are initiated and propagated by means of a set of differential equations. Usingbifurcation analysis, different oscillatory varieties of these neuronal models can be determined, allowing for the classification of types of neuronal responses. The oscillatory dynamics of neuronal spiking as identified in the Hodgkin–Huxley model closely agree with empirical findings.
In addition to periodic spiking,subthreshold membrane potential oscillations, i.e.resonance behavior that does not result in action potentials, may also contribute to oscillatory activity by facilitating synchronous activity of neighboring neurons.[34][35]
Like pacemaker neurons incentral pattern generators, subtypes of cortical cells fire bursts of spikes (brief clusters of spikes) rhythmically at preferred frequencies.[2] Bursting neurons have the potential to serve as pacemakers for synchronous network oscillations, and bursts of spikes may underlie or enhance neuronal resonance.[27] Many of these neurons can be considered intrinsic oscillators, namely, neurons that generate their oscillations intrinsically, as their oscillation frequencies can be modified by local applications of glutamate in-vivo.[36]
Apart from intrinsic properties of neurons,biological neural network properties are also an important source of oscillatory activity. Neuronscommunicate with one another via synapses and affect the timing of spike trains in the post-synaptic neurons. Depending on the properties of the connection, such as the coupling strength, time delay and whether coupling isexcitatory orinhibitory, the spike trains of the interacting neurons may becomesynchronized.[37] Neurons are locally connected, forming small clusters that are calledneural ensembles. Certain network structures promote oscillatory activity at specific frequencies. For example, neuronal activity generated by two populations of interconnectedinhibitory andexcitatory cells can show spontaneous oscillations that are described by theWilson-Cowan model.
If a group of neurons engages in synchronized oscillatory activity, the neural ensemble can be mathematically represented as a single oscillator.[26] Different neural ensembles are coupled through long-range connections and form a network of weakly coupled oscillators at the next spatial scale. Weakly coupled oscillators can generate a range of dynamics including oscillatory activity.[38] Long-range connections between different brain structures, such as thethalamus and thecortex (seethalamocortical oscillation), involve time-delays due to the finiteconduction velocity of axons. Because most connections are reciprocal, they formfeed-back loops that support oscillatory activity. Oscillations recorded from multiple cortical areas can become synchronized to formlarge-scale brain networks, whose dynamics and functional connectivity can be studied by means ofspectral analysis andGranger causality measures.[39] Coherent activity of large-scale brain activity may form dynamic links between brain areas required for the integration of distributed information.[18]
Microglia – the major immune cells of the brain – have been shown to play an important role in shaping network connectivity, and thus, influencing neuronal network oscillations bothex vivo andin vivo.[40]
In addition to fast directsynaptic interactions between neurons forming a network, oscillatory activity is regulated byneuromodulators on a much slower time scale. That is, the concentration levels of certain neurotransmitters are known to regulate the amount of oscillatory activity. For instance,GABA concentration has been shown to be positively correlated with frequency of oscillations in induced stimuli.[41] A number ofnuclei in thebrainstem have diffuse projections throughout the brain influencing concentration levels of neurotransmitters such asnorepinephrine,acetylcholine andserotonin. These neurotransmitter systems affect the physiological state, e.g.,wakefulness orarousal, and have a pronounced effect on amplitude of different brain waves, such as alpha activity.[42]
Oscillations can often be described and analyzed using mathematics. Mathematicians have identified severaldynamical mechanisms that generate rhythmicity. Among the most important areharmonic (linear) oscillators,limit cycle oscillators, and delayed-feedback oscillators.[43] Harmonic oscillations appear very frequently in nature—examples are sound waves, the motion of apendulum, and vibrations of every sort. They generally arise when a physical system is perturbed by a small degree from aminimum-energy state, and are well understood mathematically.
Noise-driven harmonic oscillators realistically simulate alpha rhythm in the waking EEG as well as slow waves and spindles in the sleep EEG. SuccessfulEEG analysis algorithms were based on such models. Several other EEG components are better described by limit-cycle or delayed-feedback oscillations.
Limit-cycle oscillations arise from physical systems that show large deviations fromequilibrium, whereas delayed-feedback oscillations arise when components of a system affect each other after significant time delays. Limit-cycle oscillations can be complex but there are powerful mathematical tools for analyzing them; the mathematics of delayed-feedback oscillations is primitive in comparison. Linear oscillators and limit-cycle oscillators qualitatively differ in terms of how they respond to fluctuations in input. In a linear oscillator, the frequency is more or less constant but the amplitude can vary greatly. In a limit-cycle oscillator, the amplitude tends to be more or less constant but the frequency can vary greatly. Aheartbeat is an example of a limit-cycle oscillation in that the frequency of beats varies widely, while each individual beat continues to pump about the same amount of blood.
Computational models adopt a variety of abstractions in order to describe complex oscillatory dynamics observed in brain activity. Many models are used in the field, each defined at a different level of abstraction and trying to model different aspects of neural systems. They range from models of the short-term behaviour of individual neurons, through models of how the dynamics ofneural circuitry arise from interactions between individual neurons, to models of how behaviour can arise from abstract neural modules that represent complete subsystems.
A model of a biological neuron is a mathematical description of the properties of nerve cells, or neurons, that is designed to accurately describe and predict its biological processes. One of the most successful neuron models is the Hodgkin–Huxley model, for whichHodgkin andHuxley won the 1963 Nobel Prize in physiology or medicine. The model is based on data from thesquid giant axon and consists of nonlinear differential equations that approximate the electrical characteristics of a neuron, including the generation and propagation ofaction potentials. The model is so successful at describing these characteristics that variations of its "conductance-based" formulation continue to be utilized in neuron models over a half a century later.[44]
The Hodgkin–Huxley model is too complicated to understand using classical mathematical techniques, so researchers often turn to simplifications such as theFitzHugh–Nagumo model and theHindmarsh–Rose model, or highly idealized neuron models such as the leaky integrate-and-fire neuron, originally developed by Lapique in 1907.[45][46] Such models only capture salient membrane dynamics such as spiking orbursting at the cost of biophysical detail, but are more computationally efficient, enabling simulations of largerbiological neural networks.
A neural network model describes a population of physically interconnected neurons or a group of disparate neurons whose inputs or signalling targets define a recognizable circuit. These models aim to describe how the dynamics of neural circuitry arise from interactions between individual neurons. Local interactions between neurons can result in the synchronization of spiking activity and form the basis of oscillatory activity. In particular, models of interactingpyramidal cells and inhibitoryinterneurons have been shown to generate brain rhythms such asgamma activity.[47] Similarly, it was shown that simulations of neural networks with a phenomenological model for neuronal response failures can predict spontaneous broadband neural oscillations.[48]
Neural field models are another important tool in studying neural oscillations and are a mathematical framework describing evolution of variables such as mean firing rate in space and time. In modeling the activity of large numbers of neurons, the central idea is to take the density of neurons to thecontinuum limit, resulting in spatially continuousneural networks. Instead of modelling individual neurons, this approach approximates a group of neurons by its average properties and interactions. It is based on themean field approach, an area ofstatistical physics that deals with large-scale systems. Models based on these principles have been used to provide mathematical descriptions of neural oscillations and EEG rhythms. They have for instance been used to investigate visual hallucinations.[50]
TheKuramoto model of coupled phase oscillators[51] is one of the most abstract and fundamental models used to investigate neural oscillations and synchronization. It captures the activity of a local system (e.g., a single neuron or neural ensemble) by its circularphase alone and hence ignores the amplitude of oscillations (amplitude is constant).[52] Interactions amongst these oscillators are introduced by a simple algebraic form (such as asine function) and collectively generate a dynamical pattern at the global scale.
The Kuramoto model is widely used to study oscillatory brain activity, and several extensions have been proposed that increase its neurobiological plausibility, for instance by incorporating topological properties of local cortical connectivity.[53] In particular, it describes how the activity of a group of interacting neurons can become synchronized and generate large-scale oscillations.
Simulations using the Kuramoto model with realistic long-range cortical connectivity and time-delayed interactions reveal the emergence of slow patterned fluctuations that reproduce resting-stateBOLD functional maps, which can be measured usingfMRI.[54]
Both single neurons and groups of neurons can generate oscillatory activity spontaneously. In addition, they may show oscillatory responses to perceptual input or motor output. Some types of neurons will fire rhythmically in the absence of any synaptic input. Likewise, brain-wide activity reveals oscillatory activity while subjects do not engage in any activity, so-calledresting-state activity. These ongoing rhythms can change in different ways in response to perceptual input or motor output. Oscillatory activity may respond by increases or decreases in frequency and amplitude or show a temporary interruption, which is referred to as phase resetting. In addition, external activity may not interact with ongoing activity at all, resulting in an additive response.
Spontaneous activity isbrain activity in the absence of an explicit task, such as sensory input or motor output, and hence also referred to as resting-state activity. It is opposed to induced activity, i.e. brain activity that is induced by sensory stimuli or motor responses.
The termongoing brain activity is used inelectroencephalography andmagnetoencephalography for those signal components that are not associated with the processing of astimulus or the occurrence of specific other events, such as moving a body part, i.e. events that do not formevoked potentials/evoked fields, or induced activity.
Spontaneous activity is usually considered to benoise if one is interested in stimulus processing; however, spontaneous activity is considered to play a crucial role during brain development, such as in network formation and synaptogenesis. Spontaneous activity may be informative regarding the current mental state of the person (e.g. wakefulness, alertness) and is often used in sleep research. Certain types of oscillatory activity, such asalpha waves, are part of spontaneous activity. Statistical analysis of power fluctuations of alpha activity reveals a bimodal distribution, i.e. a high- and low-amplitude mode, and hence shows that resting-state activity does not just reflect anoise process.[55]
In case of fMRI, spontaneous fluctuations in theblood-oxygen-level dependent (BOLD) signal reveal correlation patterns that are linked to resting state networks, such as thedefault network.[56] The temporal evolution of resting state networks is correlated with fluctuations of oscillatory EEG activity in different frequency bands.[57]
Ongoing brain activity may also have an important role in perception, as it may interact with activity related to incoming stimuli. Indeed,EEG studies suggest that visual perception is dependent on both the phase and amplitude of cortical oscillations. For instance, the amplitude and phase of alpha activity at the moment of visual stimulation predicts whether a weak stimulus will be perceived by the subject.[58][59][60]
In response to input, a neuron orneuronal ensemble may change the frequency at which it oscillates, thus changing therate at which it spikes. Often, a neuron's firing rate depends on the summed activity it receives. Frequency changes are also commonly observed incentral pattern generators and directly relate to the speed ofmotor activities, such as step frequency in walking. However, changes inrelative oscillation frequency between differentbrain areas is not so common because the frequency of oscillatory activity is often related to the time delays between brain areas.
Next to evoked activity, neural activity related to stimulus processing may result in induced activity. Induced activity refers to modulation in ongoing brain activity induced by processing of stimuli or movement preparation. Hence, they reflect an indirect response in contrast to evoked responses. A well-studied type of induced activity is amplitude change in oscillatory activity. For instance,gamma activity often increases during increased mental activity such as during object representation.[61] Because induced responses may have different phases across measurements and therefore would cancel out during averaging, they can only be obtained usingtime-frequency analysis. Induced activity generally reflects the activity of numerous neurons: amplitude changes in oscillatory activity are thought to arise from the synchronization of neural activity, for instance by synchronization of spike timing or membrane potential fluctuations of individual neurons. Increases in oscillatory activity are therefore often referred to as event-related synchronization, while decreases are referred to as event-related desynchronization (ERD).[62]
Phase resetting occurs when input to a neuron or neuronal ensemble resets the phase of ongoing oscillations.[63] It is very common in single neurons where spike timing is adjusted to neuronal input (a neuron may spike at a fixed delay in response to periodic input, which is referred to as phase locking[19]) and may also occur in neuronal ensembles when the phases of their neurons are adjusted simultaneously. Phase resetting is fundamental for the synchronization of different neurons or different brain regions[18][38] because the timing of spikes can become phase locked to the activity of other neurons.
Phase resetting also permits the study of evoked activity, a term used inelectroencephalography andmagnetoencephalography for responses in brain activity that are directly related tostimulus-related activity.Evoked potentials andevent-related potentials are obtained from an electroencephalogram by stimulus-locked averaging, i.e. averaging different trials at fixed latencies around the presentation of a stimulus. As a consequence, those signal components that are the same in each single measurement are conserved and all others, i.e. ongoing or spontaneous activity, are averaged out. That is, event-related potentials only reflect oscillations in brain activity that arephase-locked to the stimulus or event. Evoked activity is often considered to be independent from ongoing brain activity, although this is an ongoing debate.[64][65]
It has recently been proposed that even if phases are not aligned across trials, induced activity may still causeevent-related potentials because ongoing brain oscillations may not be symmetric and thus amplitude modulations may result in a baseline shift that does not average out.[66][67] This model implies that slow event-related responses, such as asymmetric alpha activity, could result from asymmetric brain oscillation amplitude modulations, such as an asymmetry of the intracellular currents that propagate forward and backward down the dendrites.[68] Under this assumption, asymmetries in the dendritic current would cause asymmetries in oscillatory activity measured by EEG and MEG, since dendritic currents in pyramidal cells are generally thought to generate EEG and MEG signals that can be measured at the scalp.[69]
Cross-frequency coupling (CFC) describes the coupling (statistical correlation) between a slow wave and a fast wave. There are many kinds, generally written as A-B coupling, meaning the A of a slow wave is coupled with the B of a fast wave. For example, phase–amplitude coupling is where the phase of a slow wave is coupled with the amplitude of a fast wave.[70]
Thetheta-gamma code is a coupling between theta wave and gamma wave in the hippocampal network. During a theta wave, 4 to 8 non-overlapping neuron ensembles are activated in sequence. This has been hypothesized to form a neural code representing multiple items in a temporal frame[71][72]
Neural synchronization can be modulated by task constraints, such asattention, and is thought to play a role infeature binding,[73]neuronal communication,[7] andmotor coordination.[9] Neuronal oscillations became a hot topic inneuroscience in the 1990s when the studies of the visual system of the brain by Gray, Singer and others appeared to support theneural binding hypothesis.[74] According to this idea, synchronous oscillations in neuronal ensembles bind neurons representing different features of an object. For example, when a person looks at a tree, visual cortex neurons representing the tree trunk and those representing the branches of the same tree would oscillate in synchrony to form a single representation of the tree. This phenomenon is best seen inlocal field potentials which reflect the synchronous activity of local groups of neurons, but has also been shown inEEG andMEG recordings providing increasing evidence for a close relation between synchronous oscillatory activity and a variety of cognitive functions such as perceptual grouping[73] and attentional top-down control.[15][13][12]
Cells in thesinoatrial node, located in theright atrium of the heart, spontaneouslydepolarize approximately 100 times per minute. Although all of the heart's cells have the ability to generate action potentials that trigger cardiac contraction, the sinoatrial node normally initiates it, simply because it generates impulses slightly faster than the other areas. Hence, these cells generate the normalsinus rhythm and are called pacemaker cells as they directly control theheart rate. In the absence of extrinsic neural and hormonal control, cells in the SA node will rhythmically discharge. The sinoatrial node is richly innervated by theautonomic nervous system, which up or down regulates the spontaneous firing frequency of the pacemaker cells.
Synchronized firing of neurons also forms the basis of periodic motor commands for rhythmic movements. These rhythmic outputs are produced by a group of interacting neurons that form a network, called acentral pattern generator. Central pattern generators are neuronal circuits that—when activated—can produce rhythmic motor patterns in the absence of sensory or descending inputs that carry specific timing information. Examples arewalking,breathing, andswimming,[75] Most evidence for central pattern generators comes from lower animals, such as thelamprey, but there is also evidence for spinal central pattern generators in humans.[76][77]
Neuronal spiking is generally considered the basis for information transfer in the brain. For such a transfer, information needs to be coded in a spiking pattern. Different types of coding schemes have been proposed, such asrate coding andtemporal coding. Neural oscillations could create periodic time windows in which input spikes have larger effect on neurons, thereby providing a mechanism for decoding temporal codes.[78]
Single-cell intrinsic oscillators serve as valuable tools for decoding temporally-encoded sensory information. This information is encoded through inter-spike intervals, and intrinsic oscillators can act as 'temporal rulers' for precisely measuring these intervals. One notable mechanism for achieving this is the neuronalphase-locked loop (NPLL). In this mechanism, cortical oscillators undergo modulation influenced by the firing rates of thalamocortical 'phase detectors,' which, in turn, gauge the disparity between the cortical and sensory periodicity.[79]
Synchronization of neuronal firing may serve as a means to group spatially segregated neurons that respond to the same stimulus in order to bind these responses for further joint processing, i.e. to exploit temporal synchrony to encode relations. Purely theoretical formulations of the binding-by-synchrony hypothesis were proposed first,[80] but subsequently extensive experimental evidence has been reported supporting the potential role of synchrony as a relational code.[81]
The functional role of synchronized oscillatory activity in the brain was mainly established in experiments performed on awake kittens with multiple electrodes implanted in the visual cortex. These experiments showed that groups of spatially segregated neurons engage in synchronous oscillatory activity when activated by visual stimuli. The frequency of these oscillations was in the range of 40 Hz and differed from the periodic activation induced by the grating, suggesting that the oscillations and their synchronization were due to internal neuronal interactions.[81] Similar findings were shown in parallel by the group of Eckhorn, providing further evidence for the functional role of neural synchronization in feature binding.[82] Since then, numerous studies have replicated these findings and extended them to different modalities such as EEG, providing extensive evidence of the functional role ofgamma oscillations in visual perception.
Gilles Laurent and colleagues showed that oscillatory synchronization has an important functional role in odor perception. Perceiving different odors leads to different subsets of neurons firing on different sets of oscillatory cycles.[83] These oscillations can be disrupted byGABA blockerpicrotoxin,[84] and the disruption of the oscillatory synchronization leads to impairment of behavioral discrimination of chemically similar odorants in bees,[85] and to more similar responses across odors in downstream β-lobe neurons.[86] Recent follow-up of this work has shown that oscillations create periodic integration windows forKenyon cells in the insectmushroom body, such that incoming spikes from theantennal lobe are more effective in activating Kenyon cells only at specific phases of the oscillatory cycle.[78]
Neural oscillations are also thought be involved in thesense of time[87] and in somatosensory perception.[88] However, recent findings argue against a clock-like function of cortical gamma oscillations.[89]
Oscillations have been commonly reported in the motor system. Pfurtscheller and colleagues found a reduction inalpha (8–12 Hz) andbeta (13–30 Hz) oscillations inEEG activity when subjects made a movement.[62][90] Using intra-cortical recordings, similar changes in oscillatory activity were found in the motor cortex when the monkeys performed motor acts that required significant attention.[91][92] In addition, oscillations at spinal level become synchronised to beta oscillations in the motor cortex during constant muscle activation, as determined bycortico-muscular coherence.[93][94][95] Likewise, muscle activity of different muscles revealsinter-muscular coherence at multiple distinct frequencies reflecting the underlyingneural circuitry involved inmotor coordination.[96][97]
Recently it was found that cortical oscillations propagate astravelling waves across the surface of the motor cortex along dominant spatial axes characteristic of the local circuitry of the motor cortex.[98] It has been proposed that motor commands in the form of travelling waves can be spatially filtered by the descending fibres to selectively control muscle force.[99] Simulations have shown that ongoing wave activity in cortex can elicit steady muscle force with physiological levels of EEG-EMG coherence.[100]
Oscillatory rhythms at 10 Hz have been recorded in a brain area called theinferior olive, which is associated with the cerebellum.[20] These oscillations are also observed in motor output of physiologicaltremor[101] and when performing slow finger movements.[102] These findings may indicate that the human brain controls continuous movements intermittently. In support, it was shown that these movement discontinuities are directly correlated to oscillatory activity in a cerebello-thalamo-cortical loop, which may represent a neural mechanism for the intermittent motor control.[103]
Neural oscillations, in particulartheta activity, are extensively linked to memory function. Theta rhythms are very strong in rodent hippocampi and entorhinal cortex during learning and memory retrieval, and they are believed to be vital to the induction oflong-term potentiation, a potential cellular mechanism for learning and memory.Coupling between theta andgamma activity is thought to be vital for memory functions, includingepisodic memory.[104][105] Tight coordination of single-neuron spikes with local theta oscillations is linked to successful memory formation in humans, as more stereotyped spiking predicts better memory.[106]
Sleep is a naturally recurring state characterized by reduced or absentconsciousness and proceeds in cycles ofrapid eye movement (REM) andnon-rapid eye movement (NREM) sleep. Sleep stages are characterized by spectral content ofEEG: for instance, stage N1 refers to the transition of the brain from alpha waves (common in the awake state) to theta waves, whereas stage N3 (deep or slow-wave sleep) is characterized by the presence of delta waves.[107] The normal order of sleep stages is N1 → N2 → N3 → N2 → REM.[citation needed]
Neural oscillations may play a role in neural development. For example,retinal waves are thought to have properties that define early connectivity of circuits and synapses between cells in the retina.[108]
Specific types of neural oscillations may also appear in pathological situations, such asParkinson's disease orepilepsy. These pathological oscillations often consist of an aberrant version of a normal oscillation. For example, one of the best known types is thespike and wave oscillation, which is typical of generalized or absence epileptic seizures, and which resembles normal sleep spindle oscillations.
A tremor is an involuntary, somewhat rhythmic, muscle contraction and relaxation involving to-and-fro movements of one or more body parts. It is the most common of all involuntary movements and can affect the hands, arms, eyes, face, head, vocal cords, trunk, and legs. Most tremors occur in the hands. In some people, tremor is a symptom of another neurological disorder. Many different forms of tremor have been identified, such asessential tremor orParkinsonian tremor. It is argued that tremors are likely to be multifactorial in origin, with contributions from neural oscillations in the central nervous systems, but also from peripheral mechanisms such as reflex loop resonances.[109]
Epilepsy is a common chronic neurological disorder characterized byseizures. These seizures are transient signs and/or symptoms of abnormal, excessive orhypersynchronous neuronal activity in the brain.[110]
In thalamocortical dysrhythmia (TCD), normalthalamocortical resonance is disrupted. The thalamic loss of input allows the frequency of the thalamo-cortical column to slow into the theta or delta band as identified by MEG and EEG by machine learning.[111] TCD can be treated withneurosurgical methods likethalamotomy.
Neural oscillations are sensitive to several drugs influencing brain activity; accordingly,biomarkers based on neural oscillations are emerging assecondary endpoints in clinical trials and in quantifying effects in pre-clinical studies. These biomarkers are often named "EEG biomarkers" or "Neurophysiological Biomarkers" and are quantified usingquantitative electroencephalography (qEEG). EEG biomarkers can be extracted from the EEG using the open-sourceNeurophysiological Biomarker Toolbox.
Neural oscillation has been applied as a control signal in variousbrain–computer interfaces (BCIs).[112] For example, a non-invasive BCI can be created by placing electrodes on the scalp and then measuring the weak electric signals. Although individual neuron activities cannot be recorded through non-invasive BCI because the skull damps and blurs the electromagnetic signals, oscillatory activity can still be reliably detected. The BCI was introduced by Vidal in 1973[113] as challenge of using EEG signals to control objects outside human body.
After the BCI challenge, in 1988, alpha rhythm was used in a brain rhythm based BCI for control of a physical object, a robot.[114][115] Alpha rhythm based BCI was the first BCI for control of a robot.[116][117] In particular, some forms of BCI allow users to control a device by measuring the amplitude of oscillatory activity in specific frequency bands, includingmu andbeta rhythms.
A non-inclusive list of types of oscillatory activity found in the central nervous system:
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