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(The principal neuron of the fascia dentata of the hippocampal region. It forms a single layer of cell bodies. Each cell has a dendritic tree that extends toward the surface. The branches are covered with dendritic spines. The input is carried by axons which arise in the nearby entorinal cortex and "perforate" the intervening cortex to make excitatory glutamatergic synapses onto the spines. The output is carried in an axon which becomes a mossy fiber as it arrives in CA3 and CA2 of the hippocampus to make glutamatergic synapes onto the spinous excrecenses of hippocampal pyramidal cells in those regions.)
| Models | Description |
| Action potential reconstitution from measured current waveforms (Alle et al. 2009) | |
| This NEURON code reconstitutes action potentials in a model of a hippocampal mossy fiber from experimentally measured sodium, potassium and calcium current waveforms as described in Alle et al. (2009). | |
| DG granule cell: I-A model (Beck et al 1992) | |
| NEURON mod files for the I-A current from the paper: Beck H, Ficker E, Heinemann U. Properties of two voltage-activated potassium currents in acutely isolated juvenile rat dentate gyrus granule cells. J. Neurophysiol. 68, 2086-2099 (1992) Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model. | |
| Dentate Gyrus Feed-forward inhibition (Ferrante et al. 2009) | |
| In this paper, the model was used to show how that FFI can change a steeply sigmoidal input-output (I/O) curve into a double-sigmoid typical of buffer systems. | |
| Dentate gyrus (Morgan et al. 2007, 2008, Santhakumar et al. 2005, Dyhrfjeld-Johnsen et al. 2007) | |
| This model was implemented by Rob Morgan in the Soltesz lab at UC Irvine. It is a scaleable model of the rat dentate gyrus including four cell types. This model runs in serial (on a single processor) and has been published at the size of 50,000 granule cells (with proportional numbers of the other cells). | |
| Dentate gyrus granule cell: calcium and calcium-dependent conductances (Aradi and Holmes 1999) | |
| We have constructed a detailed model of a hippocampal dentate granule (DG) cell that includes nine different channel types. Channel densities and distributions were chosen to reproduce reported physiological responses observed in normal solution and when blockers were applied. The model was used to explore the contribution of each channel type to spiking behavior with particular emphasis on the mechanisms underlying postspike events. ... The model was used to predict changes in channel densities that could lead to epileptogenic burst discharges and to predict the effect of altered buffering capacity on firing behavior. We conclude that the clustered spatial distributions of calcium related channels, the presence of slow delayed rectifier potassium currents in dendrites, and calcium buffering properties, together, might explain the resistance of DG cells to the development of epileptogenic burst discharges. | |
| Dentate gyrus granule cell: subthreshold signal processing (Schmidt-Hieber et al. 2007) | |
| Detailed compartmental cable models of 8 hippocampal granule cells of adult mice were obtained from dual patch-clamp whole-cell recordings and subsequent 3D reconstructions. This code allows to reproduce figures 6-8 from the paper. | |
| Dentate gyrus network model (Santhakumar et al 2005) | |
| Mossy cell loss and mossy fiber sprouting are two characteristic consequences of repeated seizures and head trauma. However, their precise contributions to the hyperexcitable state are not well understood. Because it is difficult, and frequently impossible, to independently examine using experimental techniques whether it is the loss of mossy cells or the sprouting of mossy fibers that leads to dentate hyperexcitability, we built a biophysically realistic and anatomically representative computational model of the dentate gyrus to examine this question. The 527-cell model, containing granule, mossy, basket, and hilar cells with axonal projections to the perforant-path termination zone, showed that even weak mossy fiber sprouting (10-15% of the strong sprouting observed in the pilocarpine model of epilepsy) resulted in the spread of seizure-like activity to the adjacent model hippocampal laminae after focal stimulation of the perforant path. See reference for more and details. | |
| Effects of Chloride accumulation and diffusion on GABAergic transmission (Jedlicka et al 2011) | |
| "In the CNS, prolonged activation of GABA(A) receptors (GABA(A)Rs) has been shown to evoke biphasic postsynaptic responses, consisting of an initial hyperpolarization followed by a depolarization. A potential mechanism underlying the depolarization is an acute chloride (Cl(-)) accumulation resulting in a shift of the GABA(A) reversal potential (E(GABA)). The amount of GABA-evoked Cl(-) accumulation and accompanying depolarization depends on presynaptic and postsynaptic properties of GABAergic transmission, as well as on cellular morphology and regulation of Cl(-) intracellular concentration ([Cl(-)](i)). To analyze the influence of these factors on the Cl(-) and voltage behavior, we studied spatiotemporal dynamics of activity-dependent [Cl(-)](i) changes in multicompartmental models of hippocampal cells based on realistic morphological data. ..." | |
| Epilepsy may be caused by very small functional changes in ion channels (Thomas et al. 2009) | |
| We used a previously published model of the dentate gyrus with varying degrees of mossy fibre sprouting.We preformed a sensitivity analysis where we systematically varied individual properties of ion channels. The results predict that genetic variations in the properties of sodium channels are likely to have the biggest impact on network excitability. Furthermore, these changes may be as small as 1mV, which is currently undetectable using standard experimental practices. | |
| Fast sodium channel gating in mossy fiber axons (Schmidt-Heiber et al. 2010) | |
| "... To study the mechanisms underlying AP initiation in unmyelinated hippocampal mossy fibers of adult mice, we recorded sodium currents in axonal and somatic membrane patches. We demonstrate that sodium channel density in the proximal axon is ~5 times higher than in the soma. Furthermore, sodium channel activation and inactivation are ~2 times faster. Modeling revealed that the fast activation localized the initiation site to the proximal axon even upon strong synaptic stimulation, while fast inactivation contributed to energy-efficient membrane charging during APs. ..." | |
| Na channel mutations in the dentate gyrus (Thomas et al. 2009) | |
| These are source files to generate the data in Figure 6 from "Mossy fiber sprouting interacts with sodium channel mutations to increase dentate gyrus excitability" Thomas EA, Reid CA, Petrou S, Epilepsia (2009) | |
| Shaping of action potentials by different types of BK channels (Jaffe et al., 2011) | |
| Dentate gyrus granule cells highly express the beta4 accessory subunit which confer BK channels with type II properties. The properties of heterologously-expressed BK channels (with and without the beta4 subunit) were used to construct channel models. These were then used to study how they affect single action potentials and trains of spikes in a model dentate gyrus granule cells (based on Aradi and Holmes, 1999). | |
| Sodium currents activate without a delay (Baranauskas and Martina 2006) | |
| Hodgkin and Huxley established that sodium currents in the squid giant axons activate after a delay, which is explained by the model of a channel with three identical independent gates that all have to open before the channel can pass current (the HH model). It is assumed that this model can adequately describe the sodium current activation time course in all mammalian central neurons, although there is no experimental evidence to support such a conjecture. We performed high temporal resolution studies of sodium currents gating in three types of central neurons. ... These results can be explained by a model with two closed states and one open state. ... This model captures all major properties of the sodium current activation. In addition, the proposed model reproduces the observed action potential shape more accurately than the traditional HH model. See paper for more and details. | |
| Spine fusion and branching effects synaptic response (Rusakov et al 1996, 1997) | |
| This compartmental model of a hippocampal granule cell has spinous synapses placed on the second-order dendrites. Changes in shape and connectivity of the spines usually does not effect the synaptic response of the cell unless active conductances are incorporated into the spine membrane (e.g. voltage-dependent Ca2+ channels). With active conductances, spines can generate spike-like events. We showed that changes like fusion and branching, or in fact any increase in the equivalent spine neck resistance, could trigger a dramatic increase in the spine's influence on the dendritic shaft potential. | |
| State dependent drug binding to sodium channels in the dentate gyrus (Thomas & Petrou 2013) | |
| A Markov model of sodium channels was developed that includes drug binding to fast inactivated states. This was incorporated into a model of the dentate gyrus to investigate the effects of anti-epileptic drugs on neuron and network properties. | |
| Stochastic ion channels and neuronal morphology (Cannon et al. 2010) | |
| "... We introduce and validate new computational tools that enable efficient generation and simulation of models containing stochastic ion channels distributed across dendritic and axonal membranes. Comparison of five morphologically distinct neuronal cell types reveals that when all simulated neurons contain identical densities of stochastic ion channels, the amplitude of stochastic membrane potential fluctuations differs between cell types and depends on sub-cellular location. ..." The code is downloadable and more information is available at http://www.psics.org/ | |
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