Questions for neurotwitter: 1. What are current obstacles to generating a connectomic map of a whole mammalian brain at nanometer scale? 2. What impt questions could we answer with n=1? n=2? 3. What new analysis capabilities would be needed to make sense of whole brain data?https://twitter.com/albertcardona/status/1078737475470807040 …
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Replying to @neurowitz
3). Inverse plasticity. I give you a connector and the relevant statistics of the world. I want to solve for what the brain is optimizing. It may be possible.
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Replying to @KordingLab
Do you think this is possible in the absence of functional annotations?
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Replying to @neurowitz
I think so. But it is hard.
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Replying to @KordingLab @neurowitz
We tried. Tough to infer connections, and to infer *changes* in connections is way harder. Must measure many synapses at 2 times.
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Replying to @xaqlab @KordingLab
I think
@SebastianSeung has been thinking about potential ways around this problem...1 reply 0 retweets 1 like -
Would be very curious to see his ideas.
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Replying to @KordingLab @neurowitz and
I may come across as molecular annotation extremist, but I bet with enough static molecular detail one could get something indicating dS/dt where S is synaptic strength.
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Replying to @AdamMarblestone @KordingLab and
supposing one has only a coarse anatomical "correlate" of strength (number of synapses; cumulative area of contact maybe) how good can one do at predicting circuit behavior(s)? iow, are there any cheeky shortcuts where we can do "good enough" without knowing precise strengths?
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Replying to @jipkin @AdamMarblestone and
That is a great question. And I have no idea why the connectomics field does not take it seriously!
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@tyrell_turing probably dS/dt is more important than specific strengths S in your picture, since gets more to learning rules?
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Replying to @AdamMarblestone @KordingLab and
Yes, exactly! dS/dt over multiple time scales is the key measurement, IMO, and should be a better reflection of what is being optimized. Also, note that the learn alg may involve non-synaptic modifications as well, so even if we could measure sn strengths, we might miss key info.
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Replying to @tyrell_turing @KordingLab and0 replies 0 retweets 2 likes
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