- Oral presentation
- Open Access
Mean field analysis gives accurate predictions of the behaviour of large networks of sparsely coupled and heterogeneous neurons
© Nicola et al; licensee BioMed Central Ltd. 2014
- Published: 21 July 2014
- Pyramidal Neuron
- Large Network
- Model Neuron
- Network Simulation
- Network Behaviour
Large networks of integrate-and-fire (IF) model neurons are often used to simulate and study the behaviour of biologically realistic networks. However, to fully study the large network behaviour requires an exploration of large regions of a multidimensional parameter space. Such exploration is generally not feasible with large network models, due to the computational time required to simulate a network with biologically significant size. To circumvent these difficulties we use a mean-field approach, based on the work of .
We consider a sparsely coupled, excitatory network of 10,000 Izhikevich model neurons , with Destexhe-type synapses . The cellular models were fit to hippocampal CA1 pyramidal neurons and have heterogeneous applied currents with a normal distribution. We derived a mean-field system for the network which consists of differential equations for the mean of the adaptation current and the synaptic conductance.
- Nicola W, Campbell SA: Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons. Frontiers in Computational Neuroscience. 2013, 184-10.3389/fncom.2013.00183.Google Scholar
- Izhikevich EM: Simple model of spiking neurons. IEEE Trans. on Neural Networks. 2003, 14 (6): 1569-1572. 10.1109/TNN.2003.820440.View ArticlePubMedGoogle Scholar
- Destexhe A, Mainen ZF, Sejnowski TJ: An efficient method for computing synaptic conductances based on a kinetic model of receptor binding. Neural Computation. 1994, 6: 14-18. 10.1162/neco.19188.8.131.52.View ArticleGoogle Scholar
- Goutagny R, Jackson J, Williams S: Self-generated theta oscillations in the hippocampus. Nature Neuroscience. 2009, 12 (12): 1491-1493. doi:10.1038/nn.2440View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.