Comparison of three gamma oscillations in the mouse entorhinal–hippocampal system

Butler, J. L.; Hay, Y. Audrey; Paulsen, Ole

doi:10.1111/ejn.13831

Cited by 30 publications

(54 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In such a framework, interneurons in a receiving network would preferentially resonate at a specific gamma-frequency range, establishing a communication channel not prone to interference of other inputs. Indeed, in agreement with the latter notion, multiple gamma-frequency channels have been observed in the hippocampus (Butler, Hay, & Paulsen, 2018;Colgin et al, 2009;Lopes-dos-Santos et al, 2018). Independent gamma frequency channels could arise from having different interneuron types mediating gamma-frequency oscillations (Middleton et al, 2008).…”

Section: Robustness Of Gamma Oscillations and Circuit Functionsupporting

confidence: 69%

Regulation of gamma‐frequency oscillation by feedforward inhibition: A computational modeling study

2019

View full text Add to dashboard Cite

Throughout the brain, reciprocally connected excitatory and inhibitory neurons interact to produce gamma‐frequency oscillations. The emergent gamma rhythm synchronizes local neural activity and helps to select which cells should fire in each cycle. We previously found that such excitation‐inhibition microcircuits, however, have a potentially significant caveat: the frequency of the gamma oscillation and the level of selection (i.e., the percentage of cells that are allowed to fire) vary with the magnitude of the input signal. In networks with varying levels of brain activity, such a feature may produce undesirable instability on the time and spatial structure of the neural signal with a potential for adversely impacting important neural processing mechanisms. Here we propose that feedforward inhibition solves the latter instability problem of the excitation‐inhibition microcircuit. Using computer simulations, we show that the feedforward inhibitory signal reduces the dependence of both the frequency of population oscillation and the level of selection on the magnitude of the input excitation. Such a mechanism can produce stable gamma oscillations with its frequency determined only by the properties of the feedforward network, as observed in the hippocampus. As feedforward and feedback inhibition motifs commonly appear together in the brain, we hypothesize that their interaction underlies a robust implementation of general computational principles of neural processing involved in several cognitive tasks, including the formation of cell assemblies and the routing of information between brain areas.

show abstract

Section: Robustness Of Gamma Oscillations and Circuit Functionsupporting

confidence: 69%

Regulation of gamma‐frequency oscillation by feedforward inhibition: A computational modeling study

2019

View full text Add to dashboard Cite

show abstract

“…6f). This monotonic increase in peak frequency contrasts with the properties of oscillations induced by photo-activation of principal cells in the hippocampus, where the frequency of the oscillations remains relatively constant within the slow gamma band across light intensities (Butler et al ., 2016; Betterton et al ., 2017; Butler, Hay & Paulsen, 2018). Therefore, SST+ interneuron photo-activation in CA3 appears to induce a distinct type of gamma activity.…”

Section: Resultsmentioning

confidence: 99%

“…Slow gamma oscillations in the hippocampal CA3 appear to be generated by synaptic feedback loops between excitatory pyramidal neurons and perisomatic-targeting interneurons, both in brain slices (Fisahn et al ., 1998; Hajos, 2004; Mann et al ., 2005; Oren et al ., 2006; Butler, Hay & Paulsen, 2018) and in vivo (Bragin et al ., 1995; Csicsvari et al ., 2003; Fuchs et al ., 2007). In such feedback loops, the period of the oscillation largely reflects the effective time course of inhibitory postsynaptic potentials in the pyramidal cells, which should become shorter with smaller compound inhibitory synaptic currents and/or increased pyramidal cell excitability.…”

Section: Discussionmentioning

confidence: 99%

Complementary roles for parvalbumin and somatostatin interneurons in the generation of hippocampal gamma oscillations

Mann

Antonoudiou

Tan

et al. 2019

Preprint

View full text Add to dashboard Cite

11Gamma-frequency oscillations (30-120 Hz) can be separated into fast (>60 Hz) and slow oscillations, 12 with different roles in neuronal encoding and information transfer. While synaptic inhibition is 13 important for synchronization across the gamma-frequency range, the role of distinct interneuronal 14 subtypes in fast and slow gamma states remains unclear. Here, we used optogenetics to examine 15 the involvement of parvalbumin (PV+) and somatostatin (SST+) expressing interneurons in gamma 16 oscillations in the mouse hippocampal CA3 ex vivo. Disrupting either PV+ or STT+ interneuron 17 activity, via either photo-inhibition or photo-excitation, led to a decrease in the power of 18 cholinergically-induced slow gamma oscillations. Furthermore, photo-excitation of SST+ 19 interneurons induced fast gamma oscillations, which depended on both synaptic excitation and 20 inhibition. Our findings support a critical role for both PV+ and SST+ interneurons in slow 21 hippocampal gamma oscillations, and further suggest that STT+ interneurons are capable of 22 switching the network between slow and fast gamma states. 23 24 25 26 2

show abstract

“…However, the interpretation of these experiments disagrees on the origin of the locally generated γ oscillations. Two mechanism have been suggested: namely, either inhibitory 2,42 or excitatory-inhibitory feedback loops 10,11 . Therefore, to clarify if recurrently coupled inhibitory populations, with different synaptic time scales, under a θ-drive can display θ-γ CFC we drive the slow population via an external current I (B) = I (B) 0 sin(2πν θ t) with ν θ = 10 Hz, while the rest of the parameters remains unchanged.…”

Section: Cross-frequency-coupling In Bidirectionally Coupled Populmentioning

confidence: 99%

Cross frequency coupling in next generation inhibitory neural mass models

Ceni

Olmi

Torcini

et al. 2019

Preprint

View full text Add to dashboard Cite

Coupling among neural rhythms is one of the most important mechanisms at the basis of cognitive processes in the brain. In this study we consider a neural mass model, rigorously obtained from the microscopic dynamics of an inhibitory spiking network with exponential synapses, able to autonomously generate collective oscillations (COs). These oscillations emerge via a super-critical Hopf bifurcation, and their frequencies are controlled by the synaptic time scale, the synaptic coupling and the excitability of the neural population. Furthermore, we show that two inhibitory populations in a master-slave configuration with different synaptic time scales can display various collective dynamical regimes: namely, damped oscillations towards a stable focus, periodic and quasi-periodic oscillations, and chaos. Finally, when bidirectionally coupled the two inhibitory populations can exhibit different types of θ-γ cross-frequency couplings (CFCs): namely, phase-phase and phase-amplitude CFC. The coupling between θ and γ COs is enhanced in presence of a external θ forcing, reminiscent of the type of modulation induced in Hippocampal and Cortex circuits via optogenetic drive.In healthy conditions, the brain’s activity reveals a series of intermingled oscillations, generated by large ensembles of neurons, which provide a functional substrate for information processing. How single neuron properties influence neuronal population dynamics is an unsolved question, whose solution could help in the understanding of the emergent collective behaviors arising during cognitive processes. Here we consider a neural mass model, which reproduces exactly the macroscopic activity of a network of spiking neurons. This mean-field model is employed to shade some light on an important and ubiquitous neural mechanism underlying information processing in the brain: the θ-γ cross-frequency coupling. In particular, we will explore in detail the conditions under which two coupled inhibitory neural populations can generate these functionally relevant coupled rhythms.

show abstract

Comparison of three gamma oscillations in the mouse entorhinal–hippocampal system

Cited by 30 publications

References 33 publications

Regulation of gamma‐frequency oscillation by feedforward inhibition: A computational modeling study

Regulation of gamma‐frequency oscillation by feedforward inhibition: A computational modeling study

Complementary roles for parvalbumin and somatostatin interneurons in the generation of hippocampal gamma oscillations

Cross frequency coupling in next generation inhibitory neural mass models

Contact Info

Product

Resources

About