||This package provides a series of codes that simulate networks of spiking neurons (excitatory and inhibitory, integrate-and-fire or Hodgkin-Huxley type, current-based or conductance-based synapses; some of them are event-based). The same networks are implemented in different simulators (NEURON, GENESIS, NEST, NCS, CSIM, XPP, SPLIT, MVAspike; there is also a couple of implementations in SciLab and C++).
The codes included in this package are benchmark simulations; see
the associated review paper (Brette et al. 2007). The
main goal is to provide a series of benchmark simulations of
networks of spiking neurons, and demonstrate how these are implemented in the
different simulators overviewed in the paper. See also details in the
enclosed file Appendix2.pdf, which describes these different
benchmarks. Some of these benchmarks were based on the
Vogels-Abbott model (Vogels TP and Abbott LF 2005).
||" ... We derived the (Stochastic Differential Equations) SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable – allowing an easy, transparent and efficient (Diffusion Approximation) DA implementation, avoiding unnecessary approximations. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as (Markov Chains) MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods, except when short time steps or low channel numbers were used."