The data/ subdirectory contains (for your convenience) connectomes from several other research projects. In no
The data/ subdirectory contains (for your convenience) connectomes from several other research projects. In no
particular order these are:
particular order these are:
### Data sources
# Data sources
# BBP
### BBP
data/bbp/ contains data downloadable from bbp.epfl.ch. They wish to be cited by:
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
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
...
@@ -19,17 +19,17 @@ data/bbp/ contains data downloadable from bbp.epfl.ch. They wish to be cited by:
...
@@ -19,17 +19,17 @@ data/bbp/ contains data downloadable from bbp.epfl.ch. They wish to be cited by:
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
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
### 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/c.elegans/ contains (curated) data from the wormwiring.org project. Im not sure how they wish to be cited, but the
data is from:
data is from:
https://wormwiring.org/pages/adjacency.html
https://wormwiring.org/pages/adjacency.html
# q-rewiring
### q-rewiring
These are artificial connectomes of SNN trained via Q-rewiring (Horst Petschenig). See
These are artificial connectomes of SNN trained via Q-rewiring (Horst Petschenig). See
TBA
TBA
for details.
for details.
# deep-rewiring
### deep-rewiring
These are artifical connectoms of SNN trained via deep-rewiring to solve sequential MNIST. See
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.
G. Bellec, D. Kappel, W. Maass, and R. Legenstein. Deep rewiring: training very sparse deep networks. International Conference on Learning Representations (ICLR), 2018.
