SyConn
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StructuralNeurobiologyLab
Toolkit for the generation and analysis of volume eletron microscopy based synaptic connectomes of brain tissue.
Bumps [scikit-learn](https://github.com/scikit-learn/scikit-learn) from 0.24.1 to 1.0.1. Release notes Sourced from scikit-learn's releases. scikit-learn 1.0.1 We're happy to announce the 1.0.1 release with several bugfixes: You can see the changelog here:...
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run example workflow in our system
The input of SyConn requires the segmentation of the vesicles. But how to obtain them?
Currently, the modules `cnn` and `handler/prediction.py` require an elektronn3 installation. Pytorch-trained networks can now be saved as jit models (`.pts`), which eliminates the elektronn3 dependency for `handler/prediction.py` once all models...
Building off the changes by @ada52, I adjusted some dependencies again to make installation work on my machine (Ubuntu 22.04.3 LTS). See https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/15863 for an explanation of the `setuptools
Hi, I was following the steps in https://structuralneurobiologylab.github.io/SyConn/ to install SyConn. However, when I run the line `pip install -r requirements.txt --upgrade`, i encountered an error due to python version...
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Toolkit for the generation and analysis of volume eletron microscopy based synaptic connectomes of brain tissue.