NNET: linking small- and large-scale network models

Skinner, Frances K.; Liu, Jiang Brandon

doi:10.1016/s0925-2312(02)00844-5

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…Network simulations are performed using a modified version of our in-house software, NNET (Murray 2004;Skinner and Liu 2003), which integrates the system of differential equations using CVODE (Cohen and Hindmarsh 1996). Unless otherwise stated, initial conditions (ICs) for the two-cell network simulations are:…”

Section: E T H O D Smentioning

confidence: 99%

Using Heterogeneity to Predict Inhibitory Network Model Characteristics

Skinner

Chung²,

Ncube³

et al. 2005

Journal of Neurophysiology

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From modeling studies it has been known for >10 years that purely inhibitory networks can produce synchronous output given appropriate balances of intrinsic and synaptic parameters. Several experimental studies indicate that synchronous activity produced by inhibitory networks is critical to the production of population rhythms associated with various behavioral states. Heterogeneity of inputs to inhibitory networks strongly affect their ability to synchronize. In this paper, we explore how the amount of input heterogeneity to two-cell inhibitory networks affects their dynamics. Using numerical simulations and bifurcation analyses, we find that the ability of inhibitory networks to synchronize in the face of heterogeneity depends nonmonotonically on each of the synaptic time constant, synaptic conductance and external drive parameters. Because of this, an optimal set of parameters for a given cellular model with various biophysical characteristics can be determined. We suggest that this could be a helpful approach to use in determining the importance of different, underlying biophysical details. We further find that two-cell coherence properties are maintained in larger 10-cell networks. As such, we think that a strategy of "embedding" small network dynamics in larger networks is a useful way to understand the contribution of biophysically derived parameters to population dynamics in large networks.

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Section: E T H O D Smentioning

confidence: 99%

Using Heterogeneity to Predict Inhibitory Network Model Characteristics

Skinner

Chung²,

Ncube³

et al. 2005

Journal of Neurophysiology

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Two-Cell to N-Cell Heterogeneous, Inhibitory Networks: Precise Linking of Multistable and Coherent Properties

2005

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Inhibitory networks are now recognized as being the controllers of several brain rhythms. However, experimental work with inhibitory cells is technically difficult not only because of their smaller percentage of the neuronal population, but also because of their diverse properties. As such, inhibitory network models with tight links to the experimental data are needed to understand their contributions to population rhythms. However, mathematical analyses of network models with more than two cells is challenging when the cellular models involve biophysical details. We use bifurcation analyses and simulations to show that two-cell analyses can quantitatively predict N-cell (N = 20, 50, 100) network dynamics for heterogeneous, inhibitory networks. Interestingly, multistable states in the two-cell system are manifest as different and distinct coherent network patterns in the N-cell networks for the same parameter sets.

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Reduced kinetic schemes of short-term synaptic plasticity in inhibitory network models

Murray

Skinner

2005

Neurocomputing

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NNET: linking small- and large-scale network models

Cited by 5 publications

References 21 publications

Using Heterogeneity to Predict Inhibitory Network Model Characteristics

Using Heterogeneity to Predict Inhibitory Network Model Characteristics

Two-Cell to N-Cell Heterogeneous, Inhibitory Networks: Precise Linking of Multistable and Coherent Properties

Reduced kinetic schemes of short-term synaptic plasticity in inhibitory network models

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