Extending SciUnit model testing to bifurcation models of spin-glass-like neuron network. I would propose to use model parameter optimization based on bifurcation analysis. This would use computational tools based on the theory of nonlinear dynamical systems to generate maps of parameter space that indicate the location of bifurcations at which one type of model behavior transitions into another type. These models may use many unknown parameters and specific weights. We can construct a network by fixing the known parameters and train it on input and output to determine the unknown parameters. I believe a bifurcation analysis of this model would provide solutions for sets of states for a given set of parameters. The way we adjust parameters and weights we may analyze the computations of a neural system in how it generates ideas from the organization of a network. I hope creating an algorithm of bifurcation analysis would provide insight into features of neuronal dynamics like multistability, oscillations, and symmetry.