Models that contain the Region : Unknown

(Unknown region(s) for a network model (micro-circuit)) Re-display model names without descriptions

	Models	Description
1.	Calcium waves in neuroblastoma cells (Fink et al. 2000)
		Uses a model of IP3-mediated release of Ca from endoplasmic reticulum (ER) to study how initiation and propagation of Ca waves are affected by cell geometry, spatial distributions of ER and IP3 generation, and diffusion of Ca and mobile buffer.
2.	Effects of synaptic location and timing on synaptic integration (Rall 1964)
		Reproduces figures 5 - 8 from Rall, W. Theoretical significance of dendritic trees for neuronal input-output relations. In: Neural Theory and Modeling, ed. Reiss, R.F., Palo Alto: Stanford University Press (1964).
3.	Exact mean-field models for Izhikevich networks (Chen and Campbell 2022)
		Main code on time series and bifurcation diagrams from the paper L. Chen and S. A. Campbell, Exact mean-field models for spiking neural networks with adaptation (preprint: https://arxiv.org/abs/2203.08341)
4.	Fisher and Shannon information in finite neural populations (Yarrow et al. 2012)
		Here we model populations of rate-coding neurons with bell-shaped tuning curves and multiplicative Gaussian noise. This Matlab code supports the calculation of information theoretic (mutual information, stimulus-specific information, stimulus-specific surprise) and Fisher-based measures (Fisher information, I_Fisher, SSI_Fisher) in these population models. The information theoretic measures are computed by Monte Carlo integration, which allows computationally-intensive decompositions of the mutual information to be computed for relatively large populations (hundreds of neurons).
5.	Heterogeneous axon model (Zang et al, accepted)
		The Na+ channels that are important for action potentials show rapid inactivation, a state in which they do not conduct, although the membrane potential remains depolarized. Rapid inactivation is a determinant of millisecond scale phenomena, such as spike shape and refractory period. Na+ channels also inactivate orders of magnitude more slowly, and this slow inactivation has impacts on excitability over much longer time scales than those of a single spike or a single inter-spike interval. Here, we focus on the contribution of slow inactivation to the resilience of axonal excitability when ion channels are unevenly distributed along the axon. We study models in which the voltage-gated Na+ and K+ channels are unevenly distributed along axons with different variances, capturing the heterogeneity that biological axons display. In the absence of slow inactivation, many conductance distributions result in spontaneous tonic activity. Faithful axonal propagation is achieved with the introduction of Na+ channel slow inactivation. This “normalization” effect depends on relations between the kinetics of slow inactivation and the firing frequency. Consequently, neurons with characteristically different firing frequencies will need to implement different sets of channel properties to achieve resilience. The results of this study demonstrate the importance of the intrinsic biophysical properties of ion channels in normalizing axonal function.
6.	IP3R model comparison (Hituri and Linne 2013)
		In this study, four models of IP3R (Othmer and Tang, 1993; Dawson et al., 2003; Fraiman and Dawson, 2004; Doi et al., 2005) were selected among many to examine their behavior and compare them with experimental data available in literature. The provided MATLAB script (run_IP3R_P0.m) will run the simulations and plot Figure 2A in the paper.
7.	Single neuron with dynamic ion concentrations (Cressman et al. 2009)
		These are the full and reduced models of a generic single neuron with dynamic ion concentrations as described in Cressman et al., Journal of Computational Neuroscience (2009) 26:159–170.
8.	Software for teaching neurophysiology of neuronal circuits (Grisham et al. 2008)
		"To circumvent the many problems in teaching neurophysiology as a “wet lab,” we developed SWIMMY, a virtual fish that swims by moving its virtual tail by means of a virtual neural circuit. ... Using SWIMMY, students (1) review the basics of neurophysiology, (2) identify the neurons in the circuit, (3) ascertain the neurons’ synaptic interconnections, (4) discover which cells generate the motor pattern of swimming, (5) discover how the rhythm is generated, and finally (6) use an animation that corresponds to the activity of the motoneurons to discover the behavioral effects produced by various lesions and explain them in terms of their neural underpinnings. ..."
9.	Spike Response Model simulator (Jolivet et al. 2004, 2006, 2008)
		The Spike Response Model (SRM) optimized on the experimental data in the Single-Neuron modelling Competition ( www.incf.org/community/competitions ) for edition 2007 and edition 2008. The Spike Response Model is a simplified model of neuronal excitability where current linearly integrates to an artificial threshold. After the spike, the threshold is augmented and the voltage follows a voltage kernel that is the average voltage trace during and after a spike. The parameters were chosen to best fit the observed spike times with a method outlined in Jolivet et al. (2006).
10.	Tight junction model of CNS myelinated axons (Devaux and Gow 2008)
		Two models are included: 1) a myelinated axon is represented by an equivalent circuit with a double cable design but includes a tight junction in parallel with the myelin membrane RC circuit (called double cable model, DCM). 2) a myelinated axon is represented by an equivalent circuit with a double cable design but includes a tight junction in series with the myelin RC circuit (called tight junction model, TJM). These models have been used to simulate data from compound action potentials measured in mouse optic nerve from Claudin 11-null mice in Fig. 6 of: Devaux, J.J. & Gow, A. (2008) Tight Junctions Potentiate The Insulative Properties Of Small CNS Myelinated Axons. J Cell Biol 183, 909-921.

Re-display model names without descriptions