scholarly journals Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds

2021 ◽  
Vol 15 ◽  
Author(s):  
Yuhang Cai ◽  
Tianyi Wu ◽  
Louis Tao ◽  
Zhuo-Cheng Xiao
Keyword(s):  
Model Reduction ◽  
Network Models ◽  
Gamma Oscillations ◽  
Brain Regions ◽  
Coarse Grained ◽  
Physiological Basis ◽  
Brain Functions ◽  
Gamma Frequency ◽  
Spiking Network ◽  
Reduction Methods

Gamma frequency oscillations (25–140 Hz), observed in the neural activities within many brain regions, have long been regarded as a physiological basis underlying many brain functions, such as memory and attention. Among numerous theoretical and computational modeling studies, gamma oscillations have been found in biologically realistic spiking network models of the primary visual cortex. However, due to its high dimensionality and strong non-linearity, it is generally difficult to perform detailed theoretical analysis of the emergent gamma dynamics. Here we propose a suite of Markovian model reduction methods with varying levels of complexity and apply it to spiking network models exhibiting heterogeneous dynamical regimes, ranging from nearly homogeneous firing to strong synchrony in the gamma band. The reduced models not only successfully reproduce gamma oscillations in the full model, but also exhibit the same dynamical features as we vary parameters. Most remarkably, the invariant measure of the coarse-grained Markov process reveals a two-dimensional surface in state space upon which the gamma dynamics mainly resides. Our results suggest that the statistical features of gamma oscillations strongly depend on the subthreshold neuronal distributions. Because of the generality of the Markovian assumptions, our dimensional reduction methods offer a powerful toolbox for theoretical examinations of other complex cortical spatio-temporal behaviors observed in both neurophysiological experiments and numerical simulations.

scholarly journals Coexistence of fast and slow gamma oscillations in one population of inhibitory spiking neurons

2019 ◽  
Author(s):  
Hongjie Bi ◽  
Marco Segneri ◽  
Matteo di Volo ◽  
Alessandro Torcini
Keyword(s):  
Gamma Oscillations ◽  
Brain Regions ◽  
Neural Population ◽  
Neural Firing ◽  
Phase Coupling ◽  
Population Activity ◽  
Brain Functions ◽  
Wide Range ◽  
Gamma Rhythms ◽  
Phase Phase

Oscillations are a hallmark of neural population activity in various brain regions with a spectrum covering a wide range of frequencies. Within this spectrum gamma oscillations have received particular attention due to their ubiquitous nature and to their correlation with higher brain functions. Recently, it has been reported that gamma oscillations in the hippocampus of behaving rodents are segregated in two distinct frequency bands: slow and fast. These two gamma rhythms correspond to different states of the network, but their origin has been not yet clarified. Here, we show theoretically and numerically that a single inhibitory population can give rise to coexisting slow and fast gamma rhythms corresponding to collective oscillations of a balanced spiking network. The slow and fast gamma rhythms are generated via two different mechanisms: the fast one being driven by the coordinated tonic neural firing and the slow one by endogenous fluctuations due to irregular neural activity. We show that almost instantaneous stimulations can switch the collective gamma oscillations from slow to fast and vice versa. Furthermore, to make a closer contact with the experimental observations, we consider the modulation of the gamma rhythms induced by a slower (theta) rhythm driving the network dynamics. In this context, depending on the strength of the forcing and the noise amplitude, we observe phase-amplitude and phase-phase coupling between the fast and slow gamma oscillations and the theta forcing. Phase-phase coupling reveals on average different theta-phases preferences for the two coexisting gamma rhythms joined to a wide cycle-to-cycle variability.

scholarly journals Neural network dynamics underlying gamma synchronization deficits in schizophrenia

2020 ◽  
Author(s):  
Daisuke Koshiyama ◽  
Makoto Miyakoshi ◽  
Yash B. Joshi ◽  
Juan L. Molina ◽  
Kumiko Tanaka-Koshiyama ◽  
...  
Keyword(s):  
Steady State ◽  
Neural Circuit ◽  
Gamma Oscillations ◽  
Brain Regions ◽  
Gamma Band ◽  
Causality Analysis ◽  
Response Dynamics ◽  
Gamma Frequency ◽  
Gamma Band Activity ◽  
40 Hz

AbstractGamma band (40-Hz) activity is associated with many sensory and cognitive functions, and is critical for cortico-cortical transmission and the integration of information across neural networks. The capacity to support gamma band activity can be indexed by the auditory steady-state response (ASSR); schizophrenia patients have selectively reduced synchrony to 40-Hz stimulation. While 40-Hz ASSR is a translatable electroencephalographic biomarker with emerging utility for therapeutic development for neuropsychiatric disorders, the spatiotemporal dynamics underlying the ASSR have not yet been characterized. In this study, a novel Granger causality analysis was applied to assess the propagation of gamma oscillations in response to 40-Hz steady-state stimulation across cortical sources in schizophrenia patients (n=426) and healthy comparison subjects (n=293). Results revealed distinct, hierarchically sequenced temporal and spatial response dynamics underlying gamma synchronization deficits in patients. During the response onset interval, patients exhibited abnormal connectivity of superior temporal and frontal gyri, followed by decreased information flow from superior temporal to middle cingulate gyrus. In the later (300–500 ms) interval of the ASSR response, patients showed significantly increased connectivity from superior temporal to middle frontal gyrus followed by broad failures to engage multiple prefrontal brain regions. In conclusion, these findings reveal the rapid disorganization of neural circuit functioning in response to simple gamma-frequency stimulation in schizophrenia patients. Deficits in the generation and maintenance of gamma-band oscillations in schizophrenia reflect a fundamental connectivity abnormality across a distributed network of temporo-frontal networks.

scholarly journals Firing rate models for gamma oscillations

2019 ◽  
Vol 121 (6) ◽  
pp. 2181-2190 ◽  
Author(s):  
Stephen Keeley ◽  
Áine Byrne ◽  
André Fenton ◽  
John Rinzel
Keyword(s):  
Firing Rate ◽  
Computational Models ◽  
Network Models ◽  
Gamma Oscillations ◽  
Brain Regions ◽  
Dynamic Feature ◽  
Gamma Rhythm ◽  
Firing Rate Models ◽  
General Network ◽  
Dynamic Variables

Gamma oscillations are readily observed in a variety of brain regions during both waking and sleeping states. Computational models of gamma oscillations typically involve simulations of large networks of synaptically coupled spiking units. These networks can exhibit strongly synchronized gamma behavior, whereby neurons fire in near synchrony on every cycle, or weakly modulated gamma behavior, corresponding to stochastic, sparse firing of the individual units on each cycle of the population gamma rhythm. These spiking models offer valuable biophysical descriptions of gamma oscillations; however, because they involve many individual neuronal units they are limited in their ability to communicate general network-level dynamics. Here we demonstrate that few-variable firing rate models with established synaptic timescales can account for both strongly synchronized and weakly modulated gamma oscillations. These models go beyond the classical formulations of rate models by including at least two dynamic variables per population: firing rate and synaptic activation. The models’ flexibility to capture the broad range of gamma behavior depends directly on the timescales that represent recruitment of the excitatory and inhibitory firing rates. In particular, we find that weakly modulated gamma oscillations occur robustly when the recruitment timescale of inhibition is faster than that of excitation. We present our findings by using an extended Wilson-Cowan model and a rate model derived from a network of quadratic integrate-and-fire neurons. These biophysical rate models capture the range of weakly modulated and coherent gamma oscillations observed in spiking network models, while additionally allowing for greater tractability and systems analysis. NEW & NOTEWORTHY Here we develop simple and tractable models of gamma oscillations, a dynamic feature observed throughout much of the brain with significant correlates to behavior and cognitive performance in a variety of experimental contexts. Our models depend on only a few dynamic variables per population, but despite this they qualitatively capture features observed in previous biophysical models of gamma oscillations that involve many individual spiking units.

scholarly journals The mGluR2/3 agonist LY379268 reverses NMDA receptor antagonist effects on cortical gamma oscillations and phase coherence, but not working memory impairments, in mice

2019 ◽  
Vol 33 (12) ◽  
pp. 1588-1599 ◽  
Author(s):  
Elysia Sokolenko ◽  
Matthew R Hudson ◽  
Jess Nithianantharajah ◽  
Nigel C Jones
Keyword(s):  
Working Memory ◽  
Nmda Receptor ◽  
Metabotropic Glutamate Receptor ◽  
Dorsal Hippocampus ◽  
Field Potential ◽  
Gamma Oscillations ◽  
Brain Regions ◽  
Nmda Receptor Antagonist ◽  
Mk 801 ◽  
Gamma Frequency

Background: Abnormalities in neural oscillations that occur in the gamma frequency range (30–80 Hz) may underlie cognitive deficits in schizophrenia. Both cognitive impairments and gamma oscillatory disturbances can be induced in healthy people and rodents by administration of N-methyl-D-aspartate receptor (NMDAr) antagonists. Aims: We studied relationships between cognitive impairment and gamma abnormalities following NMDAr antagonism, and attempted to reverse deficits with the metabotropic glutamate receptor type 2/3 (mGluR2/3) agonist LY379268. Methods: C57/Bl6 mice were trained to perform the Trial-Unique Nonmatching to Location (TUNL) touchscreen test for working memory. They were then implanted with local field potential (LFP) recording electrodes in prefrontal cortex and dorsal hippocampus. Mice were administered either LY379268 (3 mg/kg) or vehicle followed by the NMDAr antagonist MK-801 (0.3 or 1 mg/kg) or vehicle prior to testing on the TUNL task, or recording LFPs during the presentation of an auditory stimulus. Results: MK-801 impaired working memory and increased perseveration, but these behaviours were not improved by LY379268 treatment. MK-81 increased the power of ongoing gamma and high gamma (130–180 Hz) oscillations in both brain regions and regional coherence between regions, and these signatures were augmented by LY379268. However, auditory-evoked gamma oscillation deficits caused by MK-801 were not affected by LY379268 pretreatment. Conclusions: NMDA receptor antagonism impairs working memory in mice, but this is not reversed by stimulation of mGluR2/3. Since elevations in ongoing gamma power and regional coherence caused by MK-801 were improved by LY379268, it appears unlikely that these specific oscillatory abnormalities underlie the working memory impairment caused by NMDAr antagonism.

scholarly journals Parvalbumin interneuron dendrites enhance gamma oscillations

2021 ◽  
Author(s):  
Birgit Kriener ◽  
Hua Hu ◽  
Koen Vervaeke
Keyword(s):  
Network Models ◽  
Gamma Oscillations ◽  
Input Output ◽  
Basket Cells ◽  
Gamma Frequency ◽  
Network Oscillations ◽  
In Silico Modeling ◽  
Parvalbumin Interneuron ◽  
Relationship Of

Dendrites are important determinants of the input-output relationship of single neurons, but their role in network computations is not well understood. Here, we used a combination of dendritic patch-clamp recordings and in silico modeling to determine how dendrites of parvalbumin (PV)- expressing basket cells contribute to network oscillations in the gamma frequency band. Simultaneous soma-dendrite recordings from PV basket cells in the dentate gyrus revealed that the slope, or gain, of the dendritic input-output relationship is exceptionally low, thereby reducing the cell's sensitivity to changes in its input. By simulating gamma oscillations in detailed network models, we demonstrate that the low gain is key to increase spike synchrony in PV neuron assemblies when cells are driven by spatially and temporally heterogeneous synaptic input. These results highlight the role of dendritic computations in synchronized network oscillations.

scholarly journals Empirically Constrained Network Models for Contrast-dependent Modulation of Gamma Rhythm in V1

2019 ◽  
Author(s):  
Margarita Zachariou ◽  
Mark Roberts ◽  
Eric Lowet ◽  
Peter De Weerd ◽  
Avgis Hadjipapas
Keyword(s):  
Computational Models ◽  
Network Models ◽  
Gamma Oscillations ◽  
Gamma Frequency ◽  
Cognitive Operations ◽  
Pacemaker Function ◽  
Gamma Rhythm ◽  
Complex Models ◽  
Constrained Models ◽  
I Cells

AbstractHere we present experimentally constrained computational models of gamma rhythm and use these to investigate gamma oscillation instability. To this end, we extracted empirical constraints for PING (Pyramidal Interneuron Network Gamma) models from monkey single-unit and LFP responses recorded during contrast variation. These constraints implied weak rather than strong PING, connectivity between excitatory (E) and inhibitory (I) cells within specific bounds, and input strength variations that modulated E but not I cells. Constrained models showed valid behaviours, including gamma frequency increases with contrast and power saturation or decay at high contrasts. The route to gamma instability involved increased heterogeneity of E cells with increasing input triggering a breakdown of I cell pacemaker function. We illustrate the model’s capacity to resolve disputes in the literature. Our work is relevant for the range of cognitive operations to which gamma oscillations contribute and could serve as a basis for future, more complex models.

scholarly journals Noise promotes independent control of gamma oscillations and grid firing within recurrent attractor networks

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Lukas Solanka ◽  
Mark CW van Rossum ◽  
Matthew F Nolan
Keyword(s):  
Network Models ◽  
Intrinsic Noise ◽  
Gamma Oscillations ◽  
Synaptic Strength ◽  
Network Activity ◽  
Gamma Activity ◽  
Independent Control ◽  
Gamma Frequency ◽  
Neural Computations ◽  
Network States

Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity. However, principles relating gamma oscillations, synaptic strength and circuit computations are unclear. We address this in attractor network models that account for grid firing and theta-nested gamma oscillations in the medial entorhinal cortex. We show that moderate intrinsic noise massively increases the range of synaptic strengths supporting gamma oscillations and grid computation. With moderate noise, variation in excitatory or inhibitory synaptic strength tunes the amplitude and frequency of gamma activity without disrupting grid firing. This beneficial role for noise results from disruption of epileptic-like network states. Thus, moderate noise promotes independent control of multiplexed firing rate- and gamma-based computational mechanisms. Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength.

scholarly journals Gamma rhythm communication between entorhinal cortex and dentate gyrus neuronal assemblies

Science ◽  
2021 ◽  
Vol 372 (6537) ◽  
pp. eabf3119
Author(s):  
Antonio Fernández-Ruiz ◽  
Azahara Oliva ◽  
Marisol Soula ◽  
Florbela Rocha-Almeida ◽  
Gergo A. Nagy ◽  
...  
Keyword(s):  
Dentate Gyrus ◽  
Granule Cells ◽  
Pyramidal Cells ◽  
Gamma Oscillations ◽  
Relevant Information ◽  
Brain Regions ◽  
Spike Timing ◽  
Dependent Manner ◽  
Gamma Frequency ◽  
Gamma Rhythm

Gamma oscillations are thought to coordinate the spike timing of functionally specialized neuronal ensembles across brain regions. To test this hypothesis, we optogenetically perturbed gamma spike timing in the rat medial (MEC) and lateral (LEC) entorhinal cortices and found impairments in spatial and object learning tasks, respectively. MEC and LEC were synchronized with the hippocampal dentate gyrus through high- and low-gamma-frequency rhythms, respectively, and engaged either granule cells or mossy cells and CA3 pyramidal cells in a task-dependent manner. Gamma perturbation disrupted the learning-induced assembly organization of target neurons. Our findings imply that pathway-specific gamma oscillations route task-relevant information between distinct neuronal subpopulations in the entorhinal-hippocampal circuit. We hypothesize that interregional gamma-time-scale spike coordination is a mechanism of neuronal communication.

scholarly journals On the structural connectivity of large-scale models of brain networks at cellular level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giuseppe Giacopelli ◽  
Domenico Tegolo ◽  
Emiliano Spera ◽  
Michele Migliore
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cellular Level ◽  
Scale Model ◽  
Mathematical Framework ◽  
Underlying Mechanisms ◽  
Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

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