Huygens synchronization of medial septal pacemaker neurons generates hippocampal theta oscillation

Kocsis, Barnabás; Martínez-Bellver, Sergio; Fiáth, Richárd; Domonkos, Andor; Sviatkó, Katalin; Schlingloff, Dániel; Barthó, Péter; Freund, Tamás F.; Ulbert, István; Káli, Szabolcs; Varga, Viktor; Hangya, Balázs

doi:10.1016/j.celrep.2022.111149

Cited by 11 publications

(12 citation statements)

References 137 publications

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“…This suggests that a possible normalizing mechanism underlying the linear, proportional modulation of frequency and amplitude of the theta rhythm in the hippocampus might be an input-driven global oscillator in the medial septum, which would also modulate the oscillatory frequency of the local oscillators (Fuhrmann et al, 2015; Goutagny, Jackson, and Williams, 2009). The net input (i.e., time-varying firing rate) would in turn be the key driver of such oscillations, which is indeed consistent with the observed Huygens synchronization of medial septum’s pacemaker neurons (Kocsis et al, 2022).…”

Section: Discussionsupporting

confidence: 80%

Theta oscillations optimize a speed-precision trade-off in phase coding neurons

Amil

Albesa-González

Verschure

2022

Preprint

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Low-frequency oscillations shape how neurons sample their synaptic inputs, hence regulating information exchange among neural circuits. In the hippocampus, theta oscillations (4-8 Hz) enable the temporal organization of cortical inputs, resulting in a phase code. However, the advantages of the specific theta band over other frequency ranges remain unclear. It is possible that this specific frequency range is optimizing a trade-off between information throughput and biophysical constraints of the neuronal substrate. To test this hypothesis, we analyze a physiologically constrained model of the rodent hippocampus comprising stochastic leaky integrate-and-fire neurons driven by oscillations. By estimating the information rate of the phase code for a range of noise and frequency levels, we identify a trade-off between sampling frequency and coding precision. We observe that, under physiological noise levels, theta-band oscillations optimize the speed-precision trade-off, maximizing information rate. Further, we demonstrate that theta optimizes this trade-off throughout the entire dorsoventral axis of the hippocampus with its pronounced gradients of several physiological properties. In addition, we find that maintaining an optimal information rate relies on the concurrent modulation of both frequency and amplitude, hence explaining the locomotion speed-dependent modulations of the hippocampal theta oscillation that are observed in rodents and humans. Overall, our results suggest that low-frequency oscillations are adapted to maximize the information rate of neural sampling given the temporal and biophysical constraints under which neural circuits operate.

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Section: Discussionsupporting

confidence: 80%

Theta oscillations optimize a speed-precision trade-off in phase coding neurons

Amil

Albesa-González

Verschure

2022

Preprint

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“…Experiments on unanesthetized head-fixed mice show that there is no significant difference in their firing rate between theta and non-theta states (Simon et al, 2006 ). In a recent study, when a much larger number of neurons is considered, a certain decrease in their firing rate in the non-theta is observed (Kocsis et al, 2022 ). Experiments on anesthetized animals show a more substantial decrease in firing rate in the non-theta state, but even in this state PV GABAergic MS neurons discharge with non-zero frequency (Borhegyi et al, 2004 ; Kocsis et al, 2022 ).…”

Section: Transition Between Hippocampal Theta and Non-theta Statesmentioning

confidence: 96%

“…In a recent study, when a much larger number of neurons is considered, a certain decrease in their firing rate in the non-theta is observed (Kocsis et al, 2022). Experiments on anesthetized animals show a more substantial decrease in firing rate in the non-theta state, but even in this state PV GABAergic MS neurons discharge with non-zero frequency (Borhegyi et al, 2004;Kocsis et al, 2022). It is interesting that the firing pattern of PV GABAergic MS neurons is likely dependent on the cell population.…”

Section: Role Of the Medial Septum In The Control Of Hippocampal Non-...mentioning

confidence: 96%

“…Recording hippocampal CA1 units in awake guinea pigs, Rivas and colleagues showed that, when the animals change from the standing position to walking, the majority of units (presumed pyramidal cells) increased their rhythmicity and/or firing frequency or phase-locking with theta rhythm (Rivas et al, 1996 ). More recently it was shown that these transitions between different behavioral states with overall low oscillatory activity are accompanied by a decrease in the firing of PV basket cells, which may facilitate the network reorganization (Lapray et al, 2012 ; Kocsis et al, 2022 ).…”

Section: Transition Between Hippocampal Theta and Non-theta Statesmentioning

confidence: 99%

“…Teevra cells maintain their firing rate (Joshi et al, 2017 ), while Komal cells reduce their firing rate compared to the theta state (Unal et al, 2015 ; Joshi et al, 2017 ; Viney et al, 2018 ). At the same time, all subpopulations of PV GABAergic MS neurons decrease their rhythmicity in the absence of hippocampal theta rhythm (Joshi et al, 2017 ; Viney et al, 2018 ; Kocsis et al, 2022 ). Based on these data, we assume that in the non-theta state, the antiphase mode of discharge of Teevra and Komal cells is destroyed; i.e., Teevra cells suppress Komal cells.…”

Section: Transition Between Hippocampal Theta and Non-theta Statesmentioning

confidence: 99%

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Hippocampal non-theta state: The “Janus face” of information processing

Mysin

Shubina

2023

Front. Neural Circuits

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The vast majority of studies on hippocampal rhythms have been conducted on animals or humans in situations where their attention was focused on external stimuli or solving cognitive tasks. These studies formed the basis for the idea that rhythmical activity coordinates the work of neurons during information processing. However, at rest, when attention is not directed to external stimuli, brain rhythms do not disappear, although the parameters of oscillatory activity change. What is the functional load of rhythmical activity at rest? Hippocampal oscillatory activity during rest is called the non-theta state, as opposed to the theta state, a characteristic activity during active behavior. We dedicate our review to discussing the present state of the art in the research of the non-theta state. The key provisions of the review are as follows: (1) the non-theta state has its own characteristics of oscillatory and neuronal activity; (2) hippocampal non-theta state is possibly caused and maintained by change of rhythmicity of medial septal input under the influence of raphe nuclei; (3) there is no consensus in the literature about cognitive functions of the non-theta-non-ripple state; and (4) the antagonistic relationship between theta and delta rhythms observed in rodents is not always observed in humans. Most attention is paid to the non-theta-non-ripple state, since this aspect of hippocampal activity has not been investigated properly and discussed in reviews.

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Intracranial EEG-Based Directed Functional Connectivity in Alpha to Gamma Frequency Range Reflects Local Circuits of the Human Mesiotemporal Network

Novitskaya,

Schulze-Bonhage,

David

et al. 2024

Brain Topogr

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To date, it is largely unknown how frequency range of neural oscillations measured with EEG is related to functional connectivity. To address this question, we investigated frequency-dependent directed functional connectivity among the structures of mesial and anterior temporal network including amygdala, hippocampus, temporal pole and parahippocampal gyrus in the living human brain. Intracranial EEG recording was obtained from 19 consecutive epilepsy patients with normal anterior mesial temporal MR imaging undergoing intracranial presurgical epilepsy diagnostics with multiple depth electrodes. We assessed intratemporal bidirectional functional connectivity using several causality measures such as Granger causality (GC), directed transfer function (DTF) and partial directed coherence (PDC) in a frequency-specific way. In order to verify the obtained results, we compared the spontaneous functional networks with intratemporal effective connectivity evaluated by means of SPES (single pulse electrical stimulation) method. The overlap with the evoked network was found for the functional connectivity assessed by the GC method, most prominent in the higher frequency bands (alpha, beta and low gamma), yet vanishing in the lower frequencies. Functional connectivity assessed by means of DTF and PCD obtained a similar directionality pattern with the exception of connectivity between hippocampus and parahippocampal gyrus which showed opposite directionality of predominant information flow. Whereas previous connectivity studies reported significant divergence between spontaneous and evoked networks, our data show the role of frequency bands for the consistency of functional and evoked intratemporal directed connectivity. This has implications for the suitability of functional connectivity methods in characterizing local brain circuits.

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Huygens synchronization of medial septal pacemaker neurons generates hippocampal theta oscillation

Cited by 11 publications

References 137 publications

Theta oscillations optimize a speed-precision trade-off in phase coding neurons

Theta oscillations optimize a speed-precision trade-off in phase coding neurons

Hippocampal non-theta state: The “Janus face” of information processing

Intracranial EEG-Based Directed Functional Connectivity in Alpha to Gamma Frequency Range Reflects Local Circuits of the Human Mesiotemporal Network

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