| | Models | Description |
| 1. |
A single column thalamocortical network model (Traub et al 2005)
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To better understand population phenomena in thalamocortical neuronal ensembles,
we have constructed a preliminary network model with 3,560 multicompartment neurons
(containing soma, branching dendrites, and a portion of axon). Types of neurons included
superficial pyramids (with regular spiking [RS] and fast rhythmic bursting [FRB] firing
behaviors); RS spiny stellates; fast spiking (FS) interneurons, with basket-type and axoaxonic
types of connectivity, and located in superficial and deep cortical layers; low threshold spiking
(LTS) interneurons, that contacted principal cell dendrites; deep pyramids, that could have RS or
intrinsic bursting (IB) firing behaviors, and endowed either with non-tufted apical dendrites or
with long tufted apical dendrites; thalamocortical relay (TCR) cells; and nucleus reticularis
(nRT) cells. To the extent possible, both electrophysiology and synaptic connectivity were
based on published data, although many arbitrary choices were necessary. |
| 2. |
A unified thalamic model of multiple distinct oscillations (Li, Henriquez and Fröhlich 2017)
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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 cells, basket cells, ripples (Malerba et al 2016)
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Model of CA1 pyramidal layer Ripple activity, triggered when receiving current input (to represent CA3 sharp-waves).
Cells are Adaptive-Exponential Integrate and Fire neurons, receiving independent OU noise. |
| 4. |
Collection of simulated data from a thalamocortical network model (Glabska, Chintaluri, Wojcik 2017)
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"A major challenge in experimental data analysis
is the validation of analytical methods in a fully controlled
scenario where the justification of the interpretation can
be made directly and not just by plausibility.
...
One solution is to use simulations of realistic
models to generate ground truth data.
In neuroscience, creating such data requires plausible models of
neural activity, access to high performance computers, expertise and
time to prepare and run the simulations, and to process the output.
To facilitate such validation tests of analytical methods we provide
rich data sets including intracellular voltage traces, transmembrane
currents, morphologies, and spike times.
...
The data were generated using the
largest publicly available multicompartmental model of thalamocortical
network (Traub et al. 2005), with activity evoked by different thalamic stimuli."
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| 5. |
Dynamic cortical interlaminar interactions (Carracedo et al. 2013)
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"... Here we demonstrate the mechanism underlying a purely neocortical delta rhythm generator and show a remarkable laminar, cell subtype and local subcircuit delineation between delta
and nested theta rhythms. We show that spike timing during delta-nested theta rhythms controls an iterative, reciprocal interaction between deep and superficial cortical layers resembling the unsupervised learning processes proposed for laminar neural networks by Hinton and colleagues ... and mimicking the alternating cortical dynamics of sensory and memory processing during wakefulness."
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| 6. |
Dynamics in random NNs with multiple neuron subtypes (Pena et al 2018, Tomov et al 2014, 2016)
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"Spontaneous cortical population activity exhibits a multitude of oscillatory patterns, which often display synchrony during slow-wave sleep or under certain anesthetics and stay asynchronous during quiet wakefulness. The mechanisms behind these cortical states and transitions among them are not completely understood. Here we study spontaneous population activity patterns in random networks of spiking neurons of mixed types modeled by Izhikevich equations. Neurons are coupled by conductance-based synapses subject to synaptic noise. We localize the population activity patterns on the parameter diagram spanned by the relative inhibitory synaptic strength and the magnitude of synaptic noise. In absence of noise, networks display transient activity patterns, either oscillatory or at constant level. The effect of noise is to turn transient patterns into persistent ones: for weak noise, all activity patterns are asynchronous non-oscillatory independently of synaptic strengths; for stronger noise, patterns have oscillatory and synchrony characteristics that depend on the relative inhibitory synaptic strength. ..." |
| 7. |
Dynamics of sleep oscillations coupled to brain temperature on multiple scales (Csernai et al 2019)
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"Every form of neural activity depends on temperature, yet its
relationship to brain rhythms is poorly understood. In this work we
examined how sleep spindles are influenced by changing brain
temperatures and how brain temperature is influenced by sleep
oscillations. We employed a novel thermoelectrode designed for
measuring temperature while recording neural activity. We found that
spindle frequency is positively correlated and duration negatively
correlated with brain temperature. Local heating of the thalamus
replicated the temperature dependence of spindle parameters in the
heated area only, suggesting biophysical rather than global modulatory
mechanisms, a finding also supported by a thalamic network
model. Finally, we show that switches between oscillatory states also
influence brain temperature on a shorter and smaller scale. Epochs of
paradoxical sleep as well as the infra-slow oscillation were
associated with brain temperature fluctuations below 0.2°C. Our
results highlight that brain temperature is massively intertwined with
sleep oscillations on various time scales." |
| 8. |
Healthy and Epileptic Hippocampal Circuit (Aussel et al 2022)
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This model aims at reproducing healthy and epileptic hippocampal oscillations, and includes modeling of the sleep-wake cycle. It was used to study theta-nested gamma oscillations, sharp-wave ripple complexes, |
| 9. |
Human sleep/wake cycle (Rempe et al. 2010)
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This model simulates sleep in the human brain and is consistent with both the flip/flop concept and the two-process model of sleep regulation. The model also gives a possible mechanism for the changes in sleep timing seen in narcolepsy. |
| 10. |
Large cortex model with map-based neurons (Rulkov et al 2004)
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We develop a new computationally efficient approach for the analysis of complex large-scale neurobiological networks. Its key element is the use of a new phenomenological model of a neuron capable of replicating important spike pattern characteristics and designed in the form of a system of difference equations (a map). ... Interconnected with synaptic currents these model neurons demonstrated responses very similar to those found with Hodgkin-Huxley models and in experiments. We illustrate the efficacy of this approach in simulations of one- and two-dimensional cortical network models consisting of regular spiking neurons and fast spiking interneurons to model sleep and activated states of the thalamocortical system. See paper for more. |
| 11. |
Reconstrucing sleep dynamics with data assimilation (Sedigh-Sarvestani et al., 2012)
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We have developed a framework, based on the unscented Kalman filter, for estimating hidden states and parameters of a network model of sleep. The network model includes firing rates and neurotransmitter output of 5 cell-groups in the rat brain. |
| 12. |
Sleep-wake transitions in corticothalamic system (Bazhenov et al 2002)
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The authors investigate the transition between sleep and awake states with intracellular recordings in cats and computational models. The model describes many essential features of slow wave sleep and activated states as well as the transition between them. |
| 13. |
Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019)
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"Cerebellar Purkinje cells mediate accurate eye movement
coordination. However, it remains unclear how oculomotor
adaptation depends on the interplay between the characteristic
Purkinje cell response patterns, namely tonic, bursting, and
spike pauses. Here, a spiking cerebellar model assesses the role
of Purkinje cell firing patterns in vestibular ocular
reflex (VOR) adaptation. The model captures the cerebellar
microcircuit properties and it incorporates spike-based synaptic
plasticity at multiple cerebellar sites. ..." |
| 14. |
Spiking neuron model of the basal ganglia (Humphries et al 2006)
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A spiking neuron model of the basal ganglia (BG) circuit (striatum, STN, GP, SNr). Includes: parallel anatomical channels; tonic dopamine; dopamine receptors in striatum, STN, and GP; burst-firing in STN; GABAa, AMPA, and NMDA currents; effects of synaptic location. Model demonstrates selection and switching of input signals. Replicates experimental data on changes in slow-wave (<1 Hz) and gamma-band oscillations within BG nuclei following lesions and pharmacological manipulations. |
| 15. |
Thalamic Reticular Network (Destexhe et al 1994)
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Demo for simulating networks of thalamic reticular neurons (reproduces figures from Destexhe A et al 1994) |
| 16. |
Thalamocortical and Thalamic Reticular Network (Destexhe et al 1996)
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NEURON model of oscillations in networks of thalamocortical and thalamic reticular neurons in the ferret. (more applications for a model quantitatively identical to previous DLGN model; updated for NEURON v4 and above) |
