Optimization Strategies in Connective Field Mapping

Benchmarks for different modelling procedures in cortical connective field (CF) mapping.

Here we compare three flavors of population cortico-cortical connective field (CF) modeling. First, the former implementation in mrVista (using Matlab’s lscov) [1][2], which implemented a grid search approach. Without further optimization, the parameter space revealed by this implementation match predefined grid value predictions. Second, the implementation part of the connective field branch of the prfpy Python package [3]. Using Python scikit-learn optimization functions (implemented as part of the prfpy package), the parameter space prfpy provides also converge to predefined grid, although at a much faster rate (thanks to the CPU parallelization tool joblib`). Third, a custom version of more recent CF modelling approach (by So-Hyeon et al. (2024) [4]) that implements a derivative-free parameter (CF size) refinement approach (see here). Fourth, our custom Python implementation of automatic differentiation -powered gradient descent using TensorFlow and CUDA (partly inspired in the recent Python package braincoder) [5] to achieve highly efficient gradient descent (see here and here for a joint optimization approach that optmimzed both CF size and position). To coordinate this effort, we rely on widely used population receptive field mapping tools [6][7] and high-field 7T-MRI retinotopy data kindly made available by NeuroSpin.

How to cite

If you use this code please cite using the following information:

Gravel, N., Zhan M., Renken, R., & Cornelissen, F. W. (2025). Optimization strategies in cortical connective field mapping. Zenodo. DOI: https://doi.org/10.5281/zenodo.17373320

Alternatively, use this BibTeX entry:

@misc{Gravel_optCF_2025,
  author       = {Gravel, Nicol{\'a}s and Zhan, Minye and Renken, Remco and Cornelissen, Frans W},
  title        = {Optimization strategies in cortical connective field mapping},
  year         = {2025},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.17373320},
  url          = {https://doi.org/10.5281/zenodo.17373320}
}

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References