Currently neuronal model dt is fixed to 0.1 msec and BOLD dt is fixed to 1 msec. Users should be able to define custom dt's. They are currently hardcoded in the DerivedModel::Constants structs. In addition the integration loop expects BOLD dt to be 10x neuronal dt because there is an inner loop of 10 neuronal model integration steps within each step of BOLD model integration. Therefore, the steps to do this are:
Make dt and bold_dt a model member variable.
On Python side accept them as parameters, while ensuring they are divisible.
Calculate n_inner as bold_dt / dt and adapt the integration loop accordingly.
Currently neuronal model dt is fixed to 0.1 msec and BOLD dt is fixed to 1 msec. Users should be able to define custom dt's. They are currently hardcoded in the
DerivedModel::Constantsstructs. In addition the integration loop expects BOLD dt to be 10x neuronal dt because there is an inner loop of 10 neuronal model integration steps within each step of BOLD model integration. Therefore, the steps to do this are:dtandbold_dta model member variable.n_innerasbold_dt / dtand adapt the integration loop accordingly.