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digraph-analyzer

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    Florian Unger authored
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    Goal

    This project aims to extend the methods presented in https://www.frontiersin.org/articles/10.3389/fncom.2017.00048/full

    The data/ subdirectory contains (for your convenience) connectomes from several other research projects. In no particular order these are:

    Data sources

    BBP

    data/bbp/ contains data downloadable from bbp.epfl.ch. They wish to be cited by:

    1. Markram H, et al. (2015). Reconstruction and Simulation of Neocortical Microcircuitry. Cell 163:2, 456 - 492. doi: 10.1016/j.cell.2015.09.029

    2. Ramaswamy S, et al., (2015). The Neocortical Microcircuit Collaboration Portal: A Resource for Rat Somatosensory Cortex. Front. Neural Circuits 9:44. doi: 10.3389/fncir.2015.00044

    3. Reimann MW, et al., (2015). An Algorithm to Predict the Connectome of Neural Microcircuits. Front. Comput Neurosci. 9:28. doi: 10.3389/fncom.2015.00120

    C.Elegans

    data/c.elegans/ contains (curated) data from the wormwiring.org project. Im not sure how they wish to be cited, but the data is from: https://wormwiring.org/pages/adjacency.html

    q-rewiring

    These are artificial connectomes of SNN trained via Q-rewiring (Horst Petschenig). See TBA for details.

    deep-rewiring

    These are artifical connectoms of SNN trained via deep-rewiring to solve sequential MNIST. See G. Bellec, D. Kappel, W. Maass, and R. Legenstein. Deep rewiring: training very sparse deep networks. International Conference on Learning Representations (ICLR), 2018.