Preserving Derivative Information while Transforming Neuronal Curves¶
Athey, T. L., Tward, D. J., Mueller, U., Younes, L., Vogelstein, J. T., & Miller, M. I. (2023). Preserving Derivative Information while Transforming Neuronal Curves. arXiv:2303.09649.
First, make sure that you have installed the
The notebook that best demonstrates the code in this project is:
Figures in tha paper were made in the notebooks in
brainlit/experiments/map_neurons/and, when necessary, heavy computations were done using scripts in
brainlit/experiments/map_neurons/other. Many of the notebooks rely on data generated by scripts and therefore will not run as-is.
Nonetheless, the notebooks can serve as a reference to the interested reader. The notebooks that made the specific figures in the current version of the manuscript are:
map_neurons.ipynb: Fig. 1a-b, 2.
motivating-example.ipynb: Fig. 1e-f.
morphometrics-compare.ipynb: Fig. 3, 4.
straight-downsample.ipynb: Fig. 5.
Other files are more for scratch work, and are unlikely to be useful to new users.
Athey, T. L., Vogelstein, J. T., & Miller, M. I. (2022). Nyquist Sampling Rate for Projection Neuron Reconstruction. Poster at Society for Neuroscience Meeting.