Grabska-Barwińska, A;
Latham, PE;
(2014)
How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?
Journal of Computational Neuroscience
, 36
(3)
pp. 469-481.
10.1007/s10827-013-0481-5.
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Abstract
We use mean field techniques to compute the distribution of excitatory and inhibitory firing rates in large networks of randomly connected spiking quadratic integrate and fire neurons. These techniques are based on the assumption that activity is asynchronous and Poisson. For most parameter settings these assumptions are strongly violated; nevertheless, so long as the networks are not too synchronous, we find good agreement between mean field prediction and network simulations. Thus, much of the intuition developed for randomly connected networks in the asynchronous regime applies to mildly synchronous networks.
| Type: | Article |
|---|---|
| Title: | How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes? |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10827-013-0481-5 |
| Publisher version: | http://dx.doi.org/10.1007/s10827-013-0481-5 |
| Language: | English |
| Additional information: | © Springer Science+Business Media New York 2013 The final publication is available at Springer via http://dx.doi.org/10.1007/s10827-013-0481-5 |
| Keywords: | Recurrent network Synchronization Quadratic integrate and fire neuron Theta neuron Random networks Mean field theory |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1424874 |
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