| | 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. |
Alpha rhythm in vitro visual cortex (Traub et al 2020)
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The paper describes an experimental model of the alpha rhythm generated by layer 4 pyramidal neurons in a visual cortex slice. The simulation model is derived from that of Traub et al. (2005) J Neurophysiol, developed for thalamocortical oscillations. |
| 3. |
Axonal gap junctions produce fast oscillations in cerebellar Purkinje cells (Traub et al. 2008)
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Examines how electrical coupling between proximal axons produces fast oscillations in cerebellar Purkinje cells.
Traub RD, Middleton SJ, Knopfel T, Whittington MA (2008) Model of very fast (>75 Hz) network oscillations generated by electrical coupling between the proximal axons of cerebellar Purkinje cells. European Journal of Neuroscience. |
| 4. |
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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| 5. |
Electrodecrements in in vitro model of infantile spasms (Traub et al 2020)
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The code is an extension of the thalamocortical model of Traub et al. (2005) J Neurophysiol. It is here applied to an in vitro model of the electrodecremental response seen in the EEG of children with infantile spasms (West syndrome) |
| 6. |
Large scale neocortical model for PGENESIS (Crone et al 2019)
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This is model code for a large scale neocortical model based on Traub et al. (2005), modified to run on PGENESIS on supercomputing resources. "In this paper (Crone et al 2019), we evaluate the computational performance of the GEneral NEural SImulation System (GENESIS) for large scale simulations of neural networks. While many benchmark studies have been performed for large scale simulations with leaky integrate-and-fire neurons or neuronal models with only a few compartments, this work focuses on higher fidelity neuronal models represented by 50–74 compartments per neuron. ..." |
| 7. |
Piriform cortex network model with multicompartment neurons for cell assemblies (Traub et al 2021)
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Model contains layer 2 and layer 3 pyr cells, semilunar cells, and multiple types of superficial and deep interneurons. Used to investigate how cell assemblies and sharp waves emerge in different parameter regimes. Correlated with experimental recording. May be useful to better understand how the structure could be used for associative memory. |
| 8. |
The origin of different spike and wave-like events (Hall et al 2017)
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Acute In vitro models have revealed a great deal of information about
mechanisms underlying many types of epileptiform activity. However,
few examples exist that shed light on spike and wave (SpW) patterns of
pathological activity. SpW are seen in many epilepsy syndromes, both
generalised and focal, and manifest across the entire age
spectrum. They are heterogeneous in terms of their severity, symptom
burden and apparent anatomical origin (thalamic, neocortical or both),
but any relationship between this heterogeneity and underlying
pathology remains elusive. Here we demonstrate that physiological
delta frequency rhythms act as an effective substrate to permit
modelling of SpW of cortical origin and may help to address this
issue.
..." |
| 9. |
Unbalanced peptidergic inhibition in superficial cortex underlies seizure activity (Hall et al 2015)
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" ...Loss of tonic neuromodulatory excitation, mediated by nicotinic acetylcholine or serotonin (5HT3A) receptors, of 5HT3-immunopositive interneurons caused an increase in amplitude and slowing of the delta rhythm until each period became the "wave" component of the spike and wave discharge. As with the normal delta rhythm, the wave of a spike and wave discharge originated in cortical layer 5. In contrast, the "spike" component of the spike and wave discharge originated from a relative failure of fast inhibition in layers 2/3-switching pyramidal cell action potential outputs from single, sparse spiking during delta rhythms to brief, intense burst spiking, phase-locked to the field spike. The mechanisms underlying this loss of superficial layer fast inhibition, and a concomitant increase in slow inhibition, appeared to be precipitated by a loss of neuropeptide Y (NPY)-mediated local circuit inhibition and a subsequent increase in vasoactive intestinal peptide (VIP)-mediated disinhibition. Blockade of NPY Y1 receptors was sufficient to generate spike and wave discharges, whereas blockade of VIP receptors almost completely abolished this form of epileptiform activity. These data suggest that aberrant, activity-dependent neuropeptide corelease can have catastrophic effects on neocortical dynamics." |
