Models that contain the Model Concept : Gamma oscillations

(25-100 Hz oscillations in brains. Some investigators use 30-100 Hz for gamma.)
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    Models   Description
1.  A neural mass model of cross frequency coupling (Chehelcheraghi et al 2017)
"Electrophysiological signals of cortical activity show a range of possible frequency and amplitude modulations, both within and across regions, collectively known as cross-frequency coupling. To investigate whether these modulations could be considered as manifestations of the same underlying mechanism, we developed a neural mass model. The model provides five out of the theoretically proposed six different coupling types. ..."
2.  A unified thalamic model of multiple distinct oscillations (Li, Henriquez and Fröhlich 2017)
We present a unified model of the thalamus that is capable of independently generating multiple distinct oscillations (delta, spindle, alpha and gamma oscillations) under different levels of acetylcholine (ACh) and norepinephrine (NE) modulation corresponding to different physiological conditions (deep sleep, light sleep, relaxed wakefulness and attention). The model also shows that entrainment of thalamic oscillations is state-dependent.
3.  CA1 pyramidal neuron (Combe et al 2018)
"Gamma oscillations are thought to play a role in learning and memory. Two distinct bands, slow (25-50 Hz) and fast (65-100 Hz) gamma, have been identified in area CA1 of the rodent hippocampus. Slow gamma is phase-locked to activity in area CA3 and presumably driven by the Schaffer collaterals. We used a combination of computational modeling and in vitro electrophysiology in hippocampal slices of male rats to test whether CA1 neurons responded to Schaffer collateral stimulation selectively at slow gamma frequencies, and to identify the mechanisms involved. Both approaches demonstrated that in response to temporally precise input at Schaffer collaterals, CA1 pyramidal neurons fire preferentially in the slow gamma range regardless of whether the input is at fast or slow gamma frequencies, suggesting frequency selectivity in CA1 output with respect to CA3 input. In addition, phase-locking, assessed by the vector strength, was more precise for slow gamma than fast gamma input. ..."
4.  Current Dipole in Laminar Neocortex (Lee et al. 2013)
Laminar neocortical model in NEURON/Python, adapted from Jones et al 2009.
5.  Distance-dependent inhibition in the hippocampus (Strüber et al. 2017)
Network model of a hippocampal circuit including interneurons and principal cells. Amplitude and decay time course of inhibitory synapses can be systematically changed for different distances between connected cells. Various forms of excitatory drives can be administered to the network including spatially structured input.
6.  Effects of electric fields on cognitive functions (Migliore et al 2016)
The paper discusses the effects induced by an electric field at power lines frequency on neuronal activity during cognitive processes.
7.  Gamma genesis in the basolateral amygdala (Feng et al 2019)
Using in vitro and in vivo data we develop the first large-scale biophysically and anatomically realistic model of the basolateral amygdala nucleus (BL), which reproduces the dynamics of the in vivo local field potential (LFP). Significantly, it predicts that BL intrinsically generates the transient gamma oscillations observed in vivo. The model permitted exploration of the poorly understood synaptic mechanisms underlying gamma genesis in BL, and the model's ability to compute LFPs at arbitrary numbers of recording sites provided insights into the characteristics of the spatial properties of gamma bursts. Furthermore, we show how gamma synchronizes principal cells to overcome their low firing rates while simultaneously promoting competition, potentially impacting their afferent selectivity and efferent drive, and thus emotional behavior.
8.  Gamma-beta alternation in the olfactory bulb (David, Fourcaud-Trocmé et al., 2015)
This model, a simplified olfactory bulb network with mitral and granule cells, proposes a framework for two regimes of oscillation in the olfactory bulb: 1 - a weak inhibition regime (with no granule spike) where the network oscillates in the gamma (40-90Hz) band 2 - a strong inhibition regime (with granule spikes) where the network oscillates in the beta (15-30Hz) band. Slow modulations of sensory and centrifugal inputs, phase shifted by a quarter of cycle, possibly combined with short term depression of the mitral to granule AMPA synapse, allows the network to alternate between the two regimes as observed in anesthetized animals.
9.  Gap junction plasticity as a mechanism to regulate network-wide oscillations (Pernelle et al 2018)
10.  Hippocampal CA1 NN with spontaneous theta, gamma: full scale & network clamp (Bezaire et al 2016)
This model is a full-scale, biologically constrained rodent hippocampal CA1 network model that includes 9 cells types (pyramidal cells and 8 interneurons) with realistic proportions of each and realistic connectivity between the cells. In addition, the model receives realistic numbers of afferents from artificial cells representing hippocampal CA3 and entorhinal cortical layer III. The model is fully scaleable and parallelized so that it can be run at small scale on a personal computer or large scale on a supercomputer. The model network exhibits spontaneous theta and gamma rhythms without any rhythmic input. The model network can be perturbed in a variety of ways to better study the mechanisms of CA1 network dynamics. Also see online code at and further information at
11.  Inhibition and glial-K+ interaction leads to diverse seizure transition modes (Ho & Truccolo 2016)
"How focal seizures initiate and evolve in human neocortex remains a fundamental problem in neuroscience. Here, we use biophysical neuronal network models of neocortical patches to study how the interaction between inhibition and extracellular potassium ([K+]o) dynamics may contribute to different types of focal seizures. Three main types of propagated focal seizures observed in recent intracortical microelectrode recordings in humans were modelled ..."
12.  Large-scale model of neocortical slice in vitro exhibiting persistent gamma (Tomsett et al. 2014)
This model contains 15 neuron populations (8 excitatory, 7 inhibitory) arranged into 4 cortical layers (layer 1 empty, layers 2/3 combined). It produces a persistent gamma oscillation driven by layer 2/3. It runs using the VERTEX simulator, which is written in Matlab and is available from
13.  MDD: the role of glutamate dysfunction on Cingulo-Frontal NN dynamics (Ramirez-Mahaluf et al 2017)
" ...Currently, no mechanistic framework describes how network dynamics, glutamate, and serotonin interact to explain MDD symptoms and treatments. Here, we built a biophysical computational model of 2 areas (vACC and dlPFC) that can switch between emotional and cognitive processing. (Major Depression Disease) MDD networks were simulated by slowing glutamate decay in vACC and demonstrated sustained vACC activation. ..."
14.  Mechanisms underlying different onset patterns of focal seizures (Wang Y et al 2017)
"Focal seizures are episodes of pathological brain activity that appear to arise from a localised area of the brain. The onset patterns of focal seizure activity have been studied intensively, and they have largely been distinguished into two types { low amplitude fast oscillations (LAF), or high amplitude spikes (HAS). Here we explore whether these two patterns arise from fundamentally different mechanisms. Here, we use a previously established computational model of neocortical tissue, and validate it as an adequate model using clinical recordings of focal seizures. We then reproduce the two onset patterns in their most defining properties and investigate the possible mechanisms underlying the different focal seizure onset patterns in the model. ..."
15.  Model of the hippocampus over the sleep-wake cycle using Hodgkin-Huxley neurons (Aussel et al 2018)
" ...we propose a computational model of the hippocampal formation based on a realistic topology and synaptic connectivity, and we analyze the effect of different changes on the network, namely the variation of synaptic conductances, the variations of the CAN channel conductance and the variation of inputs. By using a detailed simulation of intracerebral recordings, we show that this is able to reproduce both the theta-nested gamma oscillations that are seen in awake brains and the sharp-wave ripple complexes measured during slow-wave sleep. The results of our simulations support the idea that the functional connectivity of the hippocampus, modulated by the sleep-wake variations in Acetylcholine concentration, is a key factor in controlling its rhythms."
16.  Olfactory Bulb mitral-granule network generates beta oscillations (Osinski & Kay 2016)
This model of the dendrodendritic mitral-granule synaptic network generates gamma and beta oscillations as a function of the granule cell excitability, which is represented by the granule cell resting membrane potential.
17.  PIR gamma oscillations in network of resonators (Tikidji-Hamburyan et al. 2015)
" ... The coupled oscillator model implemented with Wang–Buzsaki model neurons is not sufficiently robust to heterogeneity in excitatory drive, and therefore intrinsic frequency, to account for in vitro models of ING. Similarly, in a tightly synchronized regime, the stochastic population oscillator model is often characterized by sparse firing, whereas interneurons both in vivo and in vitro do not fire sparsely during gamma,but rather on average every other cycle. We substituted so-called resonator neural models, which exhibit class 2 excitability and postinhibitory rebound (PIR), for the integrators that are typically used. This results in much greater robustness to heterogeneity that actually increases as the average participation in spikes per cycle approximates physiological levels. Moreover, dynamic clamp experiments that show autapse-induced firing in entorhinal cortical interneurons support the idea that PIR can serve as a network gamma mechanism. ..."
18.  Place and grid cells in a loop (Rennó-Costa & Tort 2017)
This model implements a loop circuit between place and grid cells. The model was used to explain place cell remapping and grid cell realignment. Grid cell model as a continuous attractor network. Place cells have recurrent attractor network. Rate models implemented with E%-MAX winner-take-all network dynamics, with gamma cycle time-step.
19.  Visual physiology of the layer 4 cortical circuit in silico (Arkhipov et al 2018)
"Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo- cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. ..."

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