Models that contain the Implementer : van Elburg, Ronald A.J. [R.van.Elburg at ai.rug.nl]

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    Models   Description
1.  Continuous time stochastic model for neurite branching (van Elburg 2011)
"In this paper we introduce a continuous time stochastic neurite branching model closely related to the discrete time stochastic BES-model. The discrete time BES-model is underlying current attempts to simulate cortical development, but is difficult to analyze. The new continuous time formulation facilitates analytical treatment thus allowing us to examine the structure of the model more closely. ..."
2.  Determinants of fast calcium dynamics in dendritic spines and dendrites (Cornelisse et al. 2007)
"... Calcium influx time course and calcium extrusion rate were both in the same range for spines and dendrites when fitted with a dynamic multi-compartment model that included calcium binding kinetics and diffusion. In a subsequent analysis we used this model to investigate which parameters are critical determinants in spine calcium dynamics. The model confirmed the experimental findings: a higher SVR (surface-to-volume ratio) is not sufficient by itself to explain the faster rise time kinetics in spines, but only when paired with a lower buffer capacity in spines. Simulations at zero calcium-dye conditions show that calmodulin is more efficiently activated in spines, which indicates that spine morphology and buffering conditions in neocortical spines favor synaptic plasticity. ..."
3.  Generalized Carnevale-Hines algorithm (van Elburg and van Ooyen 2009)
Demo illustrating the behaviour of the integrate-and-fire model in the parameter regime relevant for the generalized event-based Carnevale-Hines integration scheme. The demo includes the improved implementation of the IntFire4 mechanism.
4.  Impact of dendritic size and topology on pyramidal cell burst firing (van Elburg and van Ooyen 2010)
The code provided here was written to systematically investigate which of the physical parameters controlled by dendritic morphology underlies the differences in spiking behaviour observed in different realizations of the 'ping-pong'-model. Structurally varying dendritic topology and length in a simplified model allows us to separate out the physical parameters derived from morphology underlying burst firing. To perform the parameter scans we created a new NEURON tool the MultipleRunControl which can be used to easily set up a parameter scan and write the simulation results to file. Using this code we found that not input conductance but the arrival time of the return current, as measured provisionally by the average electrotonic path length, determines whether the pyramidal cell (with ping-pong model dynamics) will burst or fire single spikes.

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