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

Changing the strength of connections between neurons is widely assumed to be the mechanism by which memory traces are encoded and stored in the central nervous system. In its most general form, the synaptic plasticity and memory hypothesis states that "activity-dependent synaptic plasticity is induced at appropriate synapses during memory formation and is both necessary and sufficient for the infor- mation storage underlying the type of memory mediated by the brain area in which that plasticity is observed." We outline a set of criteria by which this hypothesis can be judged and describe a range of experimental strategies used to investigate it. We review both classical and newly discovered properties of synaptic plasticity and stress the importance of the neural architecture and synaptic learning rules of the network in which it is embedded. The greater part of the article focuses on types of memory mediated by the hippocampus, amygdala, and cortex. We conclude that a wealth of data supports the notion that synaptic plasticity is necessary for learning and memory, but that little data currently supports the notion of sufficiency.

Synaptic plasticity and memory: an evaluation of the hypothesis

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

Acetylcholine is vital for cognitive functions of the brain. Although its actions in the individual cell are known in some detail, its effects at the network level are poorly understood. The hippocampus, which receives a major cholinergic input from the medial septum/diagonal band, is important in memory and exhibits network activity at 40 Hz during relevant behaviours. Here we show that cholinergic activation is sufficient to induce 40-Hz network oscillations in the hippocampus in vitro. Oscillatory activity is generated spontaneously in the CA3 subfield and can persist for hours. During the oscillatory state, principal neurons fire action potentials that are phase-related to the extracellular oscillation, but each neuron fires in only a small proportion of the cycles. Both excitatory and inhibitory synaptic events participate during the network oscillation in a precise temporal pattern. These results indicate that subcortical cholinergic input can control hippocampal memory processing by inducing fast network oscillations.

Cholinergic induction of network oscillations at 40 Hz in the hippocampus in vitro

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

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

Associative long-term potentiation (LTP) is the dominant model of memory related synaptic modifications in the mammalian brain (Bliss & Lomo, 1973; Bliss & Collingridge, 1993). It has been studied mainly in the hippocampus, a structure of importance for memory (Morris et al. 1983; Squire & Zola-Morgan, 1991). Induction of associative LTP requires activation of the N-methyl-D-aspartate (NMDA) receptor (Collingridge et al. 1983; Bliss & Collingridge, 1993), which serves as a molecular coincidence detector, requiring both presynaptic release of glutamate and postsynaptic depolarization for its activation (Nowak et al. 1984; Mayer et al. 1984). Thus associative LTP obeys Hebb's learning rule (Hebb, 1949), which suggests that when the pre- and postsynaptic elements are active at the same time then the synapse between them will be strengthened. Indeed, pairing of presynaptic and postsynaptic activity can, under some experimental conditions, lead to synaptic potentiation (Wigstrom et al. 1986; Magee & Johnston, 1997; Markram et al. 1997). However, the physiological activity that occurs during learning behaviours and which produces the critical activation of NMDA receptors, leading to synaptic potentiation in adult hippocampus, has not been determined.

According to a common interpretation of Hebb's learning rule, synaptic potentiation would be expected to occur following temporal coincidence of presynaptic activity and postsynaptic single action potentials. However, when a rat learns about spatial relations during active exploration of an environment, neurons with appropriate place fields, i.e. coding for the current location of the rat in space, and therefore those neurons that are likely to be involved in associative memories, typically show bursting activity repeated at theta frequency (5-12 Hz) (e.g. O'Keefe & Recce, 1993). Perhaps postsynaptic bursts bear a special significance for associative synaptic modification.

We wanted to test directly the common interpretation of Hebb's rule, by investigating whether coincident single pre- and postsynaptic action potentials are sufficient to induce LTP in hippocampal slices from adult rat. In order to investigate whether bursts have a special role in associative synaptic modification, we compared the efficacy of pairing pre- and postsynaptic single action potentials and pre- and postsynaptic bursts in inducing synaptic change using neuronal activity seen during exploratory learning.

https://physoc.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1469-7793.1999.0571p.x

Postsynaptic bursting is essential for ‘Hebbian’ induction of associative long-term potentiation at excitatory synapses in rat hippocampus

. Oscillatoryactivity is generated spontaneously in the CA3 subﬁeld and canpersist for hours. During the oscillatory state, principal neuronsﬁre action potentials that are phase-related to the extracellularoscillation, but each neuron ﬁres in only a small proportion of thecycles. Both excitatory and inhibitory synaptic events participateduring the network oscillation in a precise temporal pattern.These results indicate that subcortical cholinergic input cancontrol hippocampal memory processing by inducing fast net-work oscillations.

/pdf/cholinergic-induction-of-networkoscillations-at-40hz-in-the-3iu3dsfgh7.pdf

Fenella G. Pike

Papers

Cholinergic induction of network oscillations at 40 Hz in the hippocampus in vitro

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

Postsynaptic bursting is essential for ‘Hebbian’ induction of associative long-term potentiation at excitatory synapses in rat hippocampus

Cholinergic induction of networkoscillations at 40Hz in the hippocampus in vitro

Neuronal Mechanisms of Memory Formation: Synaptic Potentiation by Natural Patterns of Activity in the Hippocampus: Implications for Memory Formation