Brain Rhythms Have come of Age
Read:: - [ ] Buzsáki et al. (2023) - Brain rhythms have come of age ➕2023-09-11 !!2 rd citation todoist Print:: ❌ Zotero Link:: Zotero Files:: attachment Reading Note:: Web Rip:: url:: https://www.sciencedirect.com/science/article/pii/S0896627323002143
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Neuronal oscillations offer access to neuronal operations, bringing microscopic and macroscopic mechanisms, experimental methods, and explanations to a common platform. The field of brain rhythms has become the agora of discussions from temporal coordination of neuronal populations within and across brain regions to cognitive phenomena, including language and brain diseases.
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ience on the one hand and cognitive, neurology, and psychiatry on the other. The term ‘‘brain rhythm’’ has become a household word beyond neuroscience. Our community has move tp
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Studies of neuronal oscillations are as old as neuroscience itself. Yet, in-depth inquiry of neuronal oscillations had its earnest start only three decades ago. This pivotal change was mainly due to works that focused on the neuronal spike content of the various rhythms, their biophysical and circuit mechanisms, and drug responsiveness, which provided a link to circuit functions and, in turn, to cognitive phenomena. This mesoscopic link created a new platform, the field of ‘‘neuronal oscillations,’’ w
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This observeror reader-defined synchrony is critical in brain operations. If the action potentials from many upstream neurons arrive within the membrane time constant of the target (reader) neuron (t: 10–50 ms for a typical pyramidal neuron), their combined action is cooperative because each of them contributes to the discharge of the reader neuron.
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Action potentials arriving later can only contribute to initiating another action potential. Thus, from the reader neuron’s point of view, upstream partners that contribute to its spike discharge constitute a functional assembly (integrated by the membrane time constant), whereas spikes outside this time window can only be part of another assembly.
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This simple functional measure can thus both integrate and segregate upstream neurons into discrete assemblies, irrespective of whether they are interconnected or not.
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The neurophysiological basis of this hypothesis is that virtually all known network oscillations (>1 Hz) are based on inhibition, which creates temporal frames for parsing and chunking of neuronal spiking activity into cell assemblies and sequences of assemblies for the effective exchange of neuronal messages among neuronal networks.
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l messages among neuronal networks. Communications via rhythms is effective because neuronal oscillators have a separate ‘‘duty’’ or sending phase when spiking information is transferred and a perturbation or ‘‘receiving’’ phase.6 These types of oscillators can synchronize robustly and rapidly in a single cycle, making them ideal for segmentation of spiking information in both time and space. Thus, neuronal oscillations have a dual function in brain networks: they are influenced by spiking inputs and, in turn, affect timing of spike outputs. In addition, rhythm cycles across regions can provide the information about message frames.
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Brain rhythms cover more than four orders of magnitude in frequency, from the infraslow (<0.01 Hz) to ultrafast (200 Hz) rhythms, and include at least ten interactive oscillation classes. Integrated over a long temporal scale, the power distribution of the various frequencies has the appearance of 1/frequencyn ‘‘noise,’
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Brain rhythms cover more than four orders of magnitude in frequency, from the infraslow (<0.01 Hz) to ultrafast (200 Hz) rhythms, and include at least ten interactive oscillation classes. Integrated over a long temporal scale, the power distribution of the various frequencies has the appearance of 1/frequencyn ‘‘noise,’’ partly reflecting the fact that slow oscillations generate large, synchronous membrane-potential fluctuations in many neurons in brain-wide networks, whereas faster oscillations are associated with smaller changes in membrane potential in a limited number of cells that are synchronized only within a restricted neural volume.
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At the neuronal level, such multi-level nesting of phaseamplitude coupling reflects local-global computation, which is how distributed local processes are integrated into globally ordered states.
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The results of local computations are broadcast to widespread brain areas, and in the reverse direction, local computations and the direction of the activity flow of signals are coordinated by the phase of a global mechanism. We suggest that this global-local integration computation is the neurophysiological basis of various psychological constructs, known as ‘‘executive,’’ ‘‘attentional,’’ ‘‘contextual,’’ or ‘‘top-down’’ control. Prominent examples include the hippocampal theta phase coupling of neocortical gamma events so that the results of multiple neocortical computations can be delivered to the non-refractory phase of the theta cycle to integrate multiple modalities and the contextual control of primary visual cortex by higher-order areas via slower (alpha) oscillations.
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Keeping neurons in an excitable state is most effectively achieved by fluctuating the membrane potential close to the action potential threshold. In principle, this can be achieved by ‘‘noise,’’ sustained by balanced inhibitory and excitatory inputs. In line with this reasoning, improvement of behavioral performance in various tasks is often accompanied by reduced (i.e., asynchronous) spike correlation among cortical neurons. These experimental observations led to the suggestion that decorrelated spike fluctuations among neurons are advantageous for population coding because they increase the entropy and reduce the redundancy in the network and thus maximize the processing and storage capacity of neurons. This ‘‘independent’’ single-neuron view stands in contrast to the constraint of spike times by network oscillations and the postulated need for temporal coordination of neuronal assemblies to effectively discharge their downstream target partners. Oscillatory excitatory inputs with their parallel feedforward inhibition offer an economic compromise for the beneficial effect of noise by keeping networks in an excitatory state (i.e., a substitute for noise) as well as forming neuronal coalitions by shared cycle phases across the population, resulting in relatively random baseline firing of principal cells, yet synchronizing their actions when needed.
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Hippocampal theta oscillations illustrate this difference. Firing of neighboring assemblies (within successive gamma waves) in the theta cycle are correlated with the travel distance representation of the assembly pairs while the rat is walking on a track so that the assembly representing the current location of the rat fires at the trough of the theta cycle, whereas cell assemblies of the previously and subsequently visited places discharge on the descending and ascending phases (Figures 1G and 1H), spanning almost the entire theta cycle (thus forming a ‘‘neuronal letter’’).