amnsbr/cubnm

AI Summary: Enhance the cuBNM toolbox to generate an HTML report summarizing optimization results. The report should include visualizations of optimizer trajectories, parameter spaces, simulation states, FC/FCD comparisons, model descriptions, and optimal parameter tables. This aims to improve quality control by providing a comprehensive overview of each optimization run.

AI Summary: Implement linear and dummy models within the cuBNM toolbox for benchmarking purposes. This involves adding new model implementations in C++/CUDA, ensuring compatibility with the existing framework, and updating the Python interface to include these new models. Benchmarking against existing models will also be required.

AI Summary: Implement a new phase coherence similarity metric within the cuBNM toolbox to assess the goodness-of-fit (GOF) between simulated and empirical BOLD signals. This metric will serve as an alternative to the existing functional connectivity dynamics (FCD) measure, providing a more comprehensive evaluation of model accuracy.