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I think many in the neuroscience community would agree that there's a lot we can learn from connectomes, and there's a lot of value from just having a canonical
by stochastician 10y ago
I think many in the neuroscience community would agree that there's a lot we can learn from connectomes, and there's a lot of value from just having a canonical, authoritative map of the underlying neuroanatomy. Every year there are a few new neuroanatomy papers that say "surprise, area X projects to area Y!" and that's sort of embarrassing given that many people have been studying an area for 30 years and the sudden appearance of dopaminergic projections upsets all their previous models.
Also, there exist brain areas and regions where we do in fact have a few good good models, and connectomics has the potential to help us resolve them -- see http://www.nature.com/nature/journal/v500/n7461/full/nature12450.html http://www.nature.com/nature/journal/v500/n7461/full/nature1...
- apl 10y agoEven in these apparently simple feedforward sensory networks, connectomics haven't been the anticipated panacea. There's been a flurry of follow-up papers to Takemura et al., essentially refuting the suggested model. Turns out, even where they should connections don't constrain circuits to a sufficient degree.
- stochastician 10y agoAwesome, do you have a good cite for that? The last time I paid attention in this space was a year+ ago, when the vision people were trumpeting these results, so I'd love to know more about the current thinking. It's been my go-to example for "connectomics will help with some things", but I'm not a sensory physiologist.
- apl 10y agoTry this: https://www.ncbi.nlm.nih.gov/pubmed/26234212 https://www.ncbi.nlm.nih.gov/pubmed/26234212 I guess the key lesson is -- don't rely on a single approach, because its limitations may well lead you astray. Applies to connectomics, physiology, modelling, etc.