Grabska-Barwińska Agnieszka, Latham Peter E
Gatsby Computational Neuroscience Unit, University College London, London, UK,
J Comput Neurosci. 2014 Jun;36(3):469-81. doi: 10.1007/s10827-013-0481-5. Epub 2013 Oct 5.
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.
我们使用平均场技术来计算随机连接的脉冲二次积分发放神经元的大型网络中兴奋性和抑制性发放率的分布。这些技术基于活动是异步且呈泊松分布的假设。对于大多数参数设置，这些假设被严重违反；然而，只要网络不是过于同步，我们发现平均场预测与网络模拟之间有很好的一致性。因此，在异步状态下为随机连接网络所发展的许多直觉适用于轻度同步的网络。
