The connectome of an organism is a map of all neurons and their connections. This may be thought of as a graph with the neurons as nodes and synaptic connections as edges. However, to successfully simulate an organism’s brain using a connectome, more information will be needed. Here we take the term ‘connectome’ to refer to the graph and the underlying electron microscopy images of the neurons, which contain much more information.
Isn't it reasonable to assume that we would only need to scan parts of the brain and then extrapolate to model a full brain? Especially as AI improves this seems reasonable. Have there been any generative connectomics models that generate plausible brains?
Also, do we currently have "physically realistic models" of nematode worm behaviour? Seems like that would be a first step.
Zeiss will make 20k beam SEM which will suffice to get whole mouse connectome done in under a year.
This might be a dumb question but what do you do once you’ve mapped out these edges and nodes?
Isn't it reasonable to assume that we would only need to scan parts of the brain and then extrapolate to model a full brain? Especially as AI improves this seems reasonable. Have there been any generative connectomics models that generate plausible brains?
Also, do we currently have "physically realistic models" of nematode worm behaviour? Seems like that would be a first step.