The Human Neocortical Neurosolver (HNN) is a computational tool that enables scientists to study brain responses at the cellular and circuit levels. However, the current version of HNN-core has a limitation in that it cannot simulate large batches of simulations. This hinders the efficient optimization of parameters, which is crucial for accurate modeling. To address this issue, I am working on improving HNN-core by adding batch simulation functionality. I am also developing tutorials on advanced optimization techniques, specifically focusing on simulation-based inference (SBI), a deep learning-based Bayesian inference method. These enhancements will significantly contribute to the HNN's codebase, enabling researchers to extract crucial insights for developing theories about human brain response origins.