Prelude. Cycle 1. Introduction. Cycle 2. Structure defines function. Cycle 3. Diversity of cortical functions is provided by inhibition. Cycle 4. Windows on the brain. Cycle 5. A system of rhythms: from simple to complex dynamics. Cycle 6. Synchronization by oscillation. Cycle 7. The brain's default state: self-organized oscillations in rest and sleep. Cycle 8. Perturbation of the default patterns by experience. Cycle 9. The gamma buzz: gluing by oscillations in the waking brain. Cycle 10. Perceptions and actions are brain state-dependent. Cycle 11. Oscillations in the "other cortex:" navigation in real and memory space. Cycle 12. Coupling of systems by oscillations. Cycle 13. The tough problem. References.

Rhythms of the brain

Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources — including Na+ and Ca2+ spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations — can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.

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The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes

Gamma rhythms are commonly observed in many brain regions during both waking and sleep states, yet their functions and mechanisms remain a matter of debate. Here we review the cellular and synaptic mechanisms underlying gamma oscillations and outline empirical questions and controversial conceptual issues. Our main points are as follows: First, gamma-band rhythmogenesis is inextricably tied to perisomatic inhibition. Second, gamma oscillations are short-lived and typically emerge from the coordinated interaction of excitation and inhibition, which can be detected as local field potentials. Third, gamma rhythm typically concurs with irregular firing of single neurons, and the network frequency of gamma oscillations varies extensively depending on the underlying mechanism. To document gamma oscillations, efforts should be made to distinguish them from mere increases of gamma-band power and/or increased spiking activity. Fourth, the magnitude of gamma oscillation is modulated by slower rhythms. Such cross-frequency coupling may serve to couple active patches of cortical circuits. Because of their ubiquitous nature and strong correlation with the "operational modes" of local circuits, gamma oscillations continue to provide important clues about neuronal population dynamics in health and disease.

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Mechanisms of Gamma Oscillations

Gamma frequency oscillations are thought to provide a temporal structure for information processing in the brain. They contribute to cognitive functions, such as memory formation and sensory processing, and are disturbed in some psychiatric disorders. Fast-spiking, parvalbumin-expressing, soma-inhibiting interneurons have a key role in the generation of these oscillations. Experimental analysis in the hippocampus and the neocortex reveals that synapses among these interneurons are highly specialized. Computational analysis further suggests that synaptic specialization turns interneuron networks into robust gamma frequency oscillators.

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Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks

Synchronous rhythms represent a core mechanism for sculpting temporal coordination of neural activity in the brain-wide network. This review focuses on oscillations in the cerebral cortex that occur during cognition, in alert behaving conditions. Over the last two decades, experimental and modeling work has made great strides in elucidating the detailed cellular and circuit basis of these rhythms, particularly gamma and theta rhythms. The underlying physiological mechanisms are diverse (ranging from resonance and pacemaker properties of single cells to multiple scenarios for population synchronization and wave propagation), but also exhibit unifying principles. A major conceptual advance was the realization that synaptic inhibition plays a fundamental role in rhythmogenesis, either in an interneuronal network or in a reciprocal excitatory-inhibitory loop. Computational functions of synchronous oscillations in cognition are still a matter of debate among systems neuroscientists, in part because the notion of regular oscillation seems to contradict the common observation that spiking discharges of individual neurons in the cortex are highly stochastic and far from being clocklike. However, recent findings have led to a framework that goes beyond the conventional theory of coupled oscillators and reconciles the apparent dichotomy between irregular single neuron activity and field potential oscillations. From this perspective, a plethora of studies will be reviewed on the involvement of long-distance neuronal coherence in cognitive functions such as multisensory integration, working memory, and selective attention. Finally, implications of abnormal neural synchronization are discussed as they relate to mental disorders like schizophrenia and autism.

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Neurophysiological and Computational Principles of Cortical Rhythms in Cognition

Coherent network oscillations at several distinct frequencies occur during various behavioural states in animals (Buzsaki et al. 1983; Singer, 1993; Steriade et al. 1993), including humans (Berger, 1929; Kahana et al. 1999). This has led to the suggestion that these oscillations play a role in these behaviours. However, the function of this oscillatory activity in terms of neuronal signal processing remains unknown.

In the hippocampus, the CA3 subfield can intrinsically generate population oscillations at theta (4-7 Hz) (MacVicar & Tse, 1989; Cobb et al. 1999; Fellous & Sejnowski, 2000) and gamma frequencies (30-100 Hz) (Bragin et al. 1995; Fisahn et al. 1998) and such activity can propagate to the CA1 (Fisahn et al. 1998). Although pyramidal cells and some subpopulations of interneurones show intrinsic oscillations at theta frequencies (Leung & Yim, 1991; Cobb et al. 1995; Chapman & Lacaille, 1999) and other types of interneurones are known to play an important role in gamma oscillations (Whittington et al. 1995; Traub et al. 1996; Fisahn et al. 1998), the way in which network oscillations affect the activity of individual neurones in the network is unknown.

Extracellular recording of such network activity reveals oscillations which appear sinusoidal (Leung & Yim, 1986) and occur at several distinct frequencies superposed on each other (Buzsaki et al. 1983; Bragin et al. 1995). The coherent network activity, controlled by, among others, the medial septal input, contributes to the synaptic inputs on individual hippocampal neurones, which is seen collectively during intracellular recordings in vivo as sinusoidal oscillations at several distinct frequencies (Soltesz & Deschenes, 1993; Fig. 1A). An understanding of how different types of neurones within a network respond to oscillatory input patterns may provide important clues as to the roles that different cell types play in the information transfer through the network. Neurones transfer information by transforming input signals into trains of discrete action potentials. We therefore investigated the effect of sinusoidal inputs at various distinct frequencies on action potential generation in different neuronal types, and specifically asked whether different neurones exhibit any frequency preference in their response to sinusoidal input current.



Figure 1

Frequency preference of signal transfer in distinct types of hippocampal neurones



Frequency preference of action potential generation could be due to active membrane properties. An enhanced voltage response in a neuronal membrane to a narrow bandwidth of input frequencies is termed resonance (Hutcheon & Yarom, 2000). Neocortical neurones show resonant behaviour (Gutfreund et al. 1995; Hutcheon et al. 1996), and frequency preference has also been shown to exist at subthreshold membrane potentials in hippocampal pyramidal neurones (Leung & Yu, 1998). However, the frequency preferences of different types of hippocampal neurones at threshold have not been reported.

The aim of this study was to investigate how different types of neurones within the hippocampal CA1 network respond to oscillatory input patterns, by analysing their action potential discharge in response to intracellular sinusoidal current. The possible mechanisms underlying action potential transfer properties were studied by investigating the resonance properties of the neurones at membrane potentials close to the firing threshold.

https://physoc.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1469-7793.2000.00205.x

Jillian M. Suckling

Papers

Distinct frequency preferences of different types of rat hippocampal neurones in response to oscillatory input currents

Perisomatic Feedback Inhibition Underlies Cholinergically Induced Fast Network Oscillations in the Rat Hippocampus In Vitro