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AdamMarblestone's profile
Adam Marblestone
Adam Marblestone
Adam Marblestone
@AdamMarblestone

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Adam Marblestone

@AdamMarblestone

Technologist, Scientist

adammarblestone.org
Joined February 2009

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    1. Adam Marblestone‏ @AdamMarblestone 30 Oct 2019
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      Replying to @neurograce @tyrell_turing and

      My wild hot take: 1) this perspective may well be right and if so is super important 4 neuro-theory and AI — I’m investing energy on the idea that it may be right, 2) most important advance in neuro in next 5-10 years will be molecularly annotated connectome. No contradiction.

      1 reply 2 retweets 8 likes
    2. Blake Richards‏ @tyrell_turing 30 Oct 2019
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      Replying to @AdamMarblestone @neurograce and

      Totally agree!

      1 reply 1 retweet 1 like
    3. Jason Shepherd‏ @JasonSynaptic 30 Oct 2019
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      Replying to @tyrell_turing @AdamMarblestone and

      What's a molecularly annotated connectome? I'm on the side that a connectome is not going to be that useful but happy to be proved wrong. Certainly the brain has myriad *unseen* mechanisms that contribute to function but not evident in a connectome.

      2 replies 0 retweets 5 likes
    4. Michael Hendricks‏ @MHendr1cks 30 Oct 2019
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      Replying to @JasonSynaptic @tyrell_turing and

      In C. elegans, it is useful for hypothesis generation and for realizing how ubiquitous signaling relationships that ignore the connectome are. Does very little work constraining circuit models.

      1 reply 0 retweets 8 likes
    5. Adam Marblestone‏ @AdamMarblestone 30 Oct 2019
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      Replying to @MHendr1cks @JasonSynaptic and

      The best sketch I have online is this: https://arxiv.org/abs/1404.5103  Think of it as layering a lot of in-situ spatial transcriptomics & proteomics, on top of connectivity, probably all obtained optically & with the benefit of expansion microscopy... would include modulatory receptors.

      2 replies 0 retweets 6 likes
    6. WholeBrain‏ @wholebrainsuite 30 Oct 2019
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      Replying to @AdamMarblestone @MHendr1cks and

      Without a really solid foundation in molecular biology having a bunch of in situ sequencing layered on top of connectivity will be pretty 🤷🏻‍♀️. With sequencing you have to have a pretty good idea what you are looking for; e.g. antisense lncRNA and promoter choice of protocadherins.

      1 reply 0 retweets 0 likes
    7. Aaron Milstein‏ @neurosutras 30 Oct 2019
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      Replying to @wholebrainsuite @AdamMarblestone and

      I disagree. Work by Josh Huang and the Allen Institute has given characterized great markers that in conjunction help identify many inhibitory and excitatory cell types. That will go a long way towards interpreting reconstructed EM volumes.

      1 reply 0 retweets 0 likes
    8. WholeBrain‏ @wholebrainsuite 30 Oct 2019
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      Replying to @neurosutras @AdamMarblestone and

      But is the goal of molecular biology in neuroscience to identify cell types? Very depressing if so.

      1 reply 0 retweets 0 likes
    9. Adam Marblestone‏ @AdamMarblestone 30 Oct 2019
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      Replying to @wholebrainsuite @neurosutras and

      Fortunately there is a lot of mol bio of learning/memory to build on. And can compare before/after various learning. Point is you still want all this. Even if you interpret computationally by seeking ~ architectures & cost functions that will get optimized, and ask which & how.

      1 reply 0 retweets 0 likes
    10. WholeBrain‏ @wholebrainsuite 30 Oct 2019
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      Replying to @AdamMarblestone @neurosutras and

      Right. But wouldn’t one want to be way way more explicit about how one actually thinks about the mechanisms by which these architectures and their structure is brought about. Simply labeling large parts of neuroscience as stamp collectors seems unhelpful at best.

      1 reply 0 retweets 0 likes
      Adam Marblestone‏ @AdamMarblestone 30 Oct 2019
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      Replying to @wholebrainsuite @neurosutras and

      I do prefer this 2 come w/ large dose of circuit hypothesis detail. Refactor much neuro as looking 4 cost fxns, optimizers, conserved dev programs + what doesn’t fit that. As Blake’s dendritic DL work does. & yes “tuning” gives insight to it. Pytorch code=result, not sole input.

      11:30 PM - 30 Oct 2019
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        1. Adam Marblestone‏ @AdamMarblestone 30 Oct 2019
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          Replying to @AdamMarblestone @wholebrainsuite and

          (Though an appealing aspect is one can *also* try to make pytorch code based mostly on other considerations and see how it does, i.e., much current cognitively oriented AI, if you want to try to ~ignore biology.)

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        2. WholeBrain‏ @wholebrainsuite 30 Oct 2019
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          Replying to @AdamMarblestone @neurosutras and

          I whole heartedly agree.There are however some important 🛠 🧰 to be made before that can happen. Part of that is going to be pytorch-like output. Concepts might be easier to keep from AI, but I think to really get broad acceptance need to be anchored in the language we use today

          1 reply 0 retweets 2 likes
        3. Adam Marblestone‏ @AdamMarblestone 31 Oct 2019
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          Replying to @wholebrainsuite @neurosutras and

          Konrad and I tried to say similarly in 2016 but probably nobody read to page 38... https://www.biorxiv.org/content/biorxiv/early/2016/08/22/058545.full.pdf …pic.twitter.com/aHj2RmapLv

          0 replies 0 retweets 2 likes
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