Can we also map the synaptic strength with this method? If not, I think we only have half the picture; as for brains, configuration of connections are as important as layout of the circuitry.
We have less than "half the picture" here. Not just weights; also missing electrical synapses, neurotransmitters, etc. We also don't know the spatial scale of neuronal arbor integration. Furthermore these are just the image data, not the complete connectome; people still have to trace circuits by hand in this dataset. Collaborators are starting to crack the segmentation problem, but it is still early days.
Necessary but insufficient class of information!
If anyone is interested you can browse the data live here:https://fafb.catmaid.virtualflybrain.org/?pid=2&zp=131280&yp...
"URL to this view" lets you share URLs to whatever you're looking at.
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in mammals there is pretty good circumstantial evidence that post-synaptic density size correlates with evoked postsynaptic potential, but this hasn't been clearly and directly calibrated yet, and could vary from cell type to cell type
In the above examples dead brains are okay except for electrophysiology, where the brain needs to be alive.
https://ai.googleblog.com/2018/07/improving-connectomics-by-...
One of the authors even stopped by for a chat here about it.
> Next Steps
> We will continue to improve connectomics reconstruction technology, with the aim of fully automating synapse-resolution connectomics and contributing to ongoing connectomics projects at the Max Planck Institute and elsewhere. In order to help support the larger research community in developing connectomics techniques, we have also open-sourced the TensorFlow code for the flood-filling network approach, along with WebGL visualization software for 3d datasets that we developed to help us understand and improve our reconstruction results.