Our work on neuronal networks also involves two types of models: the abstract model and biophysical model. For the abstract model, we have developed moment neuronal network to deal with networks of integrate and fire models [1]. The central idea is to include mean, variance and correlation in the model. We have then applied the model to investigate various phenomena such as synfire trains etc. At the moment, work along this line is done with our Fudan colleagues (Prof. Lu WL, Dr. Liu B. et al.). Numerical approaches are used when we deal with networks with more realistic neurons [2].

Figure: A stochastic neuronal network is characterized by its first and second order statistics. For details, please see [1]

[1] Lu W.L., Rossoni E., J.F. Feng,, (2010). Toward a theory of random neuronal field model NeuroImage vol. 52, pp. 913-933.

[2] Smerieri A, Rolls ET, J.F. Feng,, (2010). Decision time, slow inhibition, and theta rhythms J. Neurosci. (in press).

[3] Vavoulis DV, Straub VA, Kemenes I, Kemenes G, Feng J, Benjamin PR (2007) Dynamic control of a central pattern generator circuit: a computational model of the snail feeding network, Eur J Neurosci. 2007 May;25(9):2805-18.