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Considerable effort is spent on large-scale neural models of brain behavior. These models are often very different and may simulate phenomena as varied as vision attention, working memory, etc. From a mathematical point of view they are usually very similar: mostly they are systems of coupled Wilson-Cowan equations, or Fokker-Plancklike equations when population density techniques (PDTs) are considered. The MIIND framework [1] allows the simulation of network processes in terms of Algorithms. The task of setting up the simulation of a largescale network becomes trivial: one only has to specify the nodes in the network and their connections and to endow the nodes with the appropriate Algorithm. Since WilsonCowan algorithms and PDTs are provided with MIIND, a large number of models described in the literature can be easily replicated. Novel simulations can be configured very quickly because of MIIND's Python interface. A novel simulation of neural dynamics (e.g. a new simulator) only needs to be defined at the node level: as soon as it is provided with an Algorithm interface, large networks can be created on the fly.

bmc neuroscience

The MIIND framework: combining population density methods, neural simulations and Wilson-Cowan dynamics into large-scale heterogeneous neural models of cognition