Neural oscillation: Wikis


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Neural oscillation is rhythmic or repetitive neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms localized within individual neurons or by feedback interactions among populations of neurons. In individual neurons, oscillations can appear either as subthreshold rhythms of membrane potential rise and fall, or as rhythmic increases and decreases in action potential activity, which then produce rhythmic activation of synapses in target neurons. At the level of neural population, synchronized oscillations of large numbers of neurons can give rise to macroscopic oscillatory electric fields, which can be observed in the electroencephalogram (EEG).



EEG signals oscillate across a spectrum of frequencies. Scientists have constructed an arbitrary set of frequency bands which group specific ranges of frequencies from this spectrum. The first discovered and best-known frequency band is alpha activity (8–12 Hz).[1] Other frequency bands are: delta (1–4 Hz), theta (4–8 Hz), beta (13–30 Hz) and gamma (30–70 Hz) frequency band. Although neural oscillations in human brain activity are mostly investigated using EEG recordings, they are also observed in animals using more invasive recording techniques such as single-unit recordings. Intracellularly, oscillations are observed in subthreshold membrane potential oscillations,[2] whereas extracellularly they are reflected in changes in local field potentials (LFPs). Large-scale oscillations that are observable outside the scalp with EEG or MEG arise through synchronous activity of large numbers of neurons.

Neural oscillations are characterized by their frequency, amplitude and phase. These signal proprieties can be extracted from neural recordings using time-frequency analysis. Changes in these characteristics have been linked to various functions. In large-scale oscillations, amplitude changes are considered to result from changes in synchronization within a neural ensemble, also referred to as local synchronization, and have been linked to cognitive functions such as perception and motor control. Apart from local synchronization, changes in the synchronization between oscillatory activity of distant neural ensembles has been observed, which might serve as a neural mechanism for information transfer.[3]

The study of neural oscillations belongs to the field of “neurodynamics”, an area of research in the cognitive sciences that places a strong focus upon the dynamic character of neural activity in describing brain function. The term neurodynamics dates back before the 1940s,[4] and is an offshoot of neuro-cybernetics using differential equations to describe neural activity patterns. Research in neurodynamics involves the interdisciplinary areas of contemporary theoretical neurobiology, nonlinear dynamics, complex adaptive systems and statistical physics. Neurodynamics is often contrasted with the popular computational and modular approaches of cognitive neuroscience, and with the implicit or explicit representationalism in cognitive science.

Neural Field Theories is a mathematical framework describing the spatio-temporal evolution of variables such as mean firing rate. In modeling the activity of large numbers of neurons, the central idea is to take the density of neurons to the continuum limit, resulting in spatially continuous neural networks. Models based on these principles provide mathematical descriptions of neural oscillations and EEG rhythms and have been used to investigate visual hallucinations,[5] and mechanisms for short-term memory and motion perception.


Mathematicians have identified several dynamical mechanisms that generate rhythmicity. Among the most important are harmonic (linear) oscillators, limit-cycle oscillators, and delayed-feedback oscillators. Harmonic (or approximately harmonic) oscillations appear very frequently in nature—examples are sound waves, waves on water, the motion of a pendulum, and vibrations of every sort. They generally arise when a physical system is perturbed by a small degree from a minimum-energy state, and are also well-understood mathematically. In neurons, however, oscillations of this type are less important than limit-cycle oscillations or delayed-feedback oscillations. Limit-cycle oscillations arise from physical systems that show large deviations from equilibrium; delayed-feedback oscillations arise when components of a system affect each other after significant time delays. Limit-cycle oscillations can be very complex but there are powerful mathematical tools for analyzing them; the mathematics of delayed-feedback oscillations is primitive in comparison.

There is an important qualitative difference between linear oscillators and limit-cycle oscillators, 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. The heartbeat, for example, behaves as 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.


Intrinsic neuronal properties

Scientists have identified some intrinsic neuronal properties that can result in 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-established Hodgkin-Huxley model that describes how action potentials are initiated and propagated by means of a set of differential equations. Using bifurcation analysis, different oscillatory regimes of these neuronal models can be determined, allowing for the classification of types of neuronal responses[6]. The oscillatory dynamics of neuronal spiking as identified using mathematical models closely agree with empirical findings. In addition to periodic spiking, subthreshold membrane potential oscillations, i.e. fluctuations that do not result in action potentials, may also contribute to oscillatory activity by facilitating synchronous activity of neighboring neurons.[7][8]

Network properties

Apart from intrinsic properties of neurons, network properties are also an important source of oscillatory activity. Neurons are locally connected, forming small clusters that are called neural ensembles. Certain network structures promote oscillatory activity at specific frequencies. This is determined by the type of neurons, i.e. excitatory or inhibitory neurons, time delays and the coupling function. Central pattern generators are a well-known example of neural networks that can endogenously produce rhythmic patterned outputs. Neural synchronization is the process by which the activity of two or more neurons or neural ensembles tend to oscillate with a repeating sequence of relative phase angles. Mathematically, neural ensembles can be considered weakly coupled oscillators, a type of system that readily allows for synchronized oscillatory activity.[9][10]

Synchronized activity of a large number of neurons results in electromagnetic fields that can be measured outside the scalp with electroencephalography and magnetoencephalography. Using these techniques, synchronized neural activity have been observed throughout the central nervous system and during various tasks. Neural synchronization can be modulated by task constraints, such as attention, and is thought to play a role in feature binding,[11] neuronal communication,[12] and motor coordination.[13]

Large-scale connections

Connections between different brain structures, for instance the thalamus and the cortex, can form loops that support oscillatory activity. Oscillations recorded from multiple cortical areas can become synchronized and form a large-scale network, whose dynamics and functional connectivity can be studied by means of spectral analysis and Granger causality measures.[14] Coherent activity of large-scale brain activity might form dynamic links between brain areas required for the integration of distributed information.[15]


Certain neurotransmitters are known to regulate the amount of oscillatory activity. GABA concentrations has been shown to be positively correlated with frequency of oscillations in induced stimuli. The exact relationship, however, can only be resolved with further pharmacological research on how GABA concentrations affect oscillatory dynamics of single neurons and local field potentials of ensembles of neurons.[16]

Activity patterns

Spontaneous activity

Spontaneous activity is brain activity in the absence of an explicit task, such as sensory input or motor output. It is opposed to induced activity, i.e. brain activity that is induced by sensory stimuli or motor responses. The term ongoing brain activity is used in electroencephalography and magnetoencephalography for those signal components that are not associated with the processing of a stimulus or the occurrence of specific other events, such as moving a body part, i.e. that do not form evoked potentials/evoked fields, event-related potentials, or induced activity.The spontaneous activity is usually considered to be noise if one is interested in stimulus processing, but might 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 as alpha waves, are part of the spontaneous activity.

Most neuroscience studies have focused on the brain’s response to a task or stimulus. However, the brain is very active even in the absence of explicit input or output. Spontaneous activity is investigated using a paradigm that requires subjects to open and close their eyes at fixed intervals while fMRI or EEG activity is recorded. In case of fMRI, spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal reveal correlation patterns that are linked to different resting states[17]. In EEG research, spontaneous fluctuations of oscillatory activity are investigated and power changed in different EEG bands show correlations with the distributed patterns of fMRI activity[18]. Research on spontaneous activity led to the hypothesis that specific brain regions constitute a network supporting a default mode of brain functioning[19].

The ongoing electroencephalogram (EEG) and magnetoencephalogram (MEG) provide a satisfactory scale for accessing temporal evolution of the brain activity associated with cognitive processes in health and disease. However, momentary (temporal) changes in brain activity, as reflected in EEG/MEG, are rarely exploited due to lack of analytical tools and methodology. Special techniques such as microstructural EEG/MEG analyses are required for the detection of such dynamics[20].

Induced activity

The term induced activity is used in electroencephalography and magnetoencephalography for certain types of stimulus-related activity. The following explanation is for electroencephalographic activity, but the concept is the same in magnetoencephalography.

Evoked potentials and event-related potentials are obtained from the electroencephalogram by stimulus-locked averaging. As a consequence, those signal components that are the same in each single measurement are conserved and all others average out. This is the wanted effect to eliminate the spontaneous brain activity. But there might also be signals that are related to stimulus processing, but are different each time. An example is oscillatory activity (e.g. gamma oscillations), which might have a different phase in each single measurement and therefore would cancel out during averaging.


Visual system

Neuronal oscillations became a hot topic in neuroscience in the 1990s when the studies of the visual system of the brain by Gray, Singer and others appeared to support the neural binding hypothesis.[21] 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. Some scientists have questioned whether these oscillations are prominent, or relevant, in ensembles that consider only action potential activity.[22] These oscillations are, however, prominent in differential LFP recordings taken between upper and lower cortical layers, which suggests a local current, but not action potential, basis for their origin.[23]

EEG studies suggest that visual perception is phase dependent as well as amplitude dependent. In a study in which human subjects were stimulated with flashes of light, it was found that phase dependence accounted for 16% of variability in the measured response to stimuli. The results suggest that ongoing oscillations provide a temporal reference for visual perception via precise spike timing.[24] EEG evidence also suggests that local oscillatory bursts display significant patterns of synchrony. During flickering light simulation of human subjects, quantifiable oscillatory patterns of synchronicity had significantly higher degrees of co-occurrence during stimulation than the background levels of synchronicity. This suggests that during visual stimulation, oscillatory patterns are reorganized in the visual cortex and propagate throughout the brain.[25]

Evidence suggests that the visual system of children is less entrained by incoming information resulting in less synchronized neural responses. Adults primarily rely on sparse representations formed through experience based temporally synchronized neural interactions. In older age, declines in neuronal density and neurotransmitter chemicals increase the reliance on temporally synchronizing processing.[26]

Other perceptual systems

Neural oscillations may have different functional roles in different brain areas, and their functional role continues to be a matter of debate. Neural oscillations have been hypothesized to be involved in the sense of time[27] and in somatosensory perception[28] among other functions.

Gilles Laurent and colleagues that showed oscillatory synchronization has an important functional role in odor perception and identified some mechanisms by which this function is established. That is, different odors lead to different subsets of neurons firing on different sets of oscillatory cycles[29] and the oscillations can be disrupted by GABA blocker picrotoxin.[30] Disruption of the oscillatory synchronization leads to impairment of behavioral discrimination of chemically similar odorants in bees[31] and to more similar responses across odors in downstream β-lobe neurons.[32]

Motor system

Oscillations have been commonly reported in the motor system. Pfurtscheller and colleagues found a reduction in alpha (8–12 Hz) and beta (13–30 Hz) oscillations in EEG activity when subjects made a movement.[33][34] Using intra-cortical recordings, Murthy and Fetz found similar oscillations in monkey cortex when the monkeys performed motor acts that required significant attention (retrieval of raisins from unseen locations).[35] Similar findings were reported by the groups of John Donoghue and Roger Lemon.[36][37] Recently it was found that these oscillations propagate as waves across the surface of the motor cortex along dominant spatial axes characteristic of the local circuitry of the motor cortex.[38]

Oscillatory rhythms at 10 Hz have been recorded in inferior olive and might be central in motor timing.[39] These oscillations are also observed in motor output of physiological tremor[40] and when performing slow finger movements.[41] These findings might indicate that the human brain controls continuous movements intermittently. In support, it was shown that 6- to 9-Hz pulsatile velocity changes of slow finger movements are directly correlated to oscillatory activity in a cerebello-thalamo-cortical loop that might represent a neural mechanism for the intermittent motor control.[42]


Neural oscillations are extensively linked to memory function, in particular theta activity. Theta rhythms are very strong in rodent hippocampi and entorhinal cortex during learning and memory retrieval, and are believed to be vital to the induction of long-term potentiation, a potential cellular mechanism of learning and memory. Recently, the coupling between theta and gamma activity is thought to be vital for memory functions.[43]


Brain-computer interface

Pesaran and colleagues[44] suggested that neural oscillations can be used as a control signal for brain-computer interfaces because oscillatory pattern depends on the direction of movement that the monkey prepares to execute. Recent study of Rickert and colleagues[45] supports this suggestion.

Pathological oscillations

Specific types of neural oscillations may also appear in pathological situations, such as Parkinson's disease or epilepsy. Interestingly, these pathological oscillations often consist of a "perverted" version of a normal oscillation. For example, one of the best known type is the Spike and Wave oscillation, which is typical of generalized or absence epileptic seizures, and which mechanisms are very close to that of the sleep spindle oscillations (see details in the Spike-and-wave Oscillations article in Scholarpedia).

See also


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