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| Models | Description |
| Amyloid beta (IA block) effects on a model CA1 pyramidal cell (Morse et al. 2010) | |
| The model simulations provide evidence oblique dendrites in CA1 pyramidal neurons are susceptible to hyper-excitability by amyloid beta block of the transient K+ channel, IA. See paper for details. | |
| 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. | |
| Compartmentalization of GABAergic inhibition by dendritic spines (Chiu et al. 2013) | |
| A spiny dendrite model supports the hypothesis that only inhibitory inputs on spine heads, not shafts, compartmentalizes inhibition of calcium signals to spine heads as seen in paired inhibition with back-propagating action potential experiments on prefrontal cortex layer 2/3 pyramidal neurons in mouse (Chiu et al. 2013). | |
| Dentate Basket Cell: spatial summation of inhibitory synaptic inputs (Bartos et al 2001) | |
| Spatial summation of inhibitory synaptic input in a passive model of a basket cell from the dentate gyrus of rat hippocampus. Reproduces Figs. 5Ac and d in Bartos, M., Vida, I., Frotscher, M., Geiger, J.R.P, and Jonas, P.. Rapid signaling at inhibitory synapses in a dentate gyrus interneuron network. Journal of Neuroscience 21:2687-2698, 2001. | |
| 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). | |
| Fly lobular plate VS cell (Borst and Haag 1996, et al. 1997, et al. 1999) | |
| In a series of papers the authors conducted experiments to develop understanding and models of fly visual system HS, CS, and VS neurons. This model recreates the VS neurons from those papers with enough success to merit approval by Borst although some discrepancies remain (see readme). | |
| Functional structure of mitral cell dendritic tuft (Djurisic et al. 2008) | |
| The computational modeling component of Djurisic et al. 2008 addressed two primary questions: whether amplification by active currents is necessary to explain the relatively mild attenuation suffered by tuft EPSPs spreading along the primary dendrite to the soma; what accounts for the relatively uniform peak EPSP amplitude throughout the tuft. These simulations show that passive spread from tuft to soma is sufficient to yield the low attenuation of tuft EPSPs, and that random distribution of a biologically plausible number of excitatory synapses throughout the tuft can produce the experimentally observed uniformity of depolarization. | |
| Irregular oscillations produced by cyclic recurrent inhibition (Friesen, Friesen 1994) | |
| Model of recurrent cyclic inhibition as described on p.119 of Friesen and Friesen (1994), which was slightly modified from Szekely's model (1965) of a network for producing alternating limb movements. | |
| Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007) | |
| This package provides a series of codes that simulate networks of spiking neurons (excitatory and inhibitory, integrate-and-fire or Hodgkin-Huxley type, current-based or conductance-based synapses; some of them are event-based). The same networks are implemented in different simulators (NEURON, GENESIS, NEST, NCS, CSIM, XPP, SPLIT, MVAspike; there is also a couple of implementations in SciLab and C++). The codes included in this package are benchmark simulations; see the associated review paper Brette et al. (2007) available at this link http://arxiv.org/abs/q-bio.NC/0611089 The main goal is to provide a series of benchmark simulations of networks of spiking neurons, and demonstrate how these are implemented in the different simulators overviewed in the paper. See also details in the enclosed file Appendix2.pdf, which describes these different benchmarks. Some of these benchmarks were based on the Vogels-Abbott model (Vogels TP and Abbott LF 2005). | |
| Short term plasticity of synapses onto V1 layer 2/3 pyramidal neuron (Varela et al 1997) | |
| This archive contains 3 mod files for NEURON that implement the short term synaptic plasticity model described in Varela, J.A., Sen, K., Gibson, J., Fost, J., Abbott, L.R., and Nelson, S.B.. A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex. Journal of Neuroscience 17:7926-7940, 1997. Contact ted.carnevale@yale.edu if you have questions about this implementation of the model. | |
| Spiny neuron model with dopamine-induced bistability (Gruber et al 2003) | |
| These files implement a model of dopaminergic modulation of voltage-gated currents (called kir2 and caL in the original paper). See spinycell.html for details of usage and implementation. For questions about this implementation, contact Ted Carnevale (ted.carnevale@yale.edu) | |
| Spontaneous firing caused by stochastic channel gating (Chow, White 1996) | |
| NEURON implementation of model of stochastic channel gating, resulting in spontaneous firing. Qualitatively reproduces the phenomena described in the reference. | |
| Synaptic plasticity: pyramid->pyr and pyr->interneuron (Tsodyks et al 1998) | |
| An implementation of a model of short-term synaptic plasticity with NEURON. The model was originally described by Tsodyks et al., who assumed that the synapse acted as a current source, but this implementation treats it as a conductance change. Tsodyks, M., Pawelzik, K., Markram, H. Neural networks with dynamic synapses. Neural Computation 10:821-835, 1998. Tsodyks, M., Uziel, A., Markram, H. Synchrony generation in recurrent networks with frequency-dependent synapses. J. Neurosci. 2000 RC50. | |
| Translating network models to parallel hardware in NEURON (Hines and Carnevale 2008) | |
| Shows how to move a working network model written in NEURON from a serial processor to a parallel machine in such a way that the final result will produce numerically identical results on either serial or parallel hardware. | |
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