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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. Michael Hendricks‏ @MHendr1cks 30 Oct 2019
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      Replying to @MHendr1cks @KordingLab and

      But tbh when I have seen the claim "we found nodes in our network with response properties that look like neuron classes" it's usually so general that it's not clear how it couldn't be true, because the classes are OFF / ON / Nothing.

      2 replies 0 retweets 2 likes
    2. Blake Richards‏ @tyrell_turing 30 Oct 2019
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      Replying to @MHendr1cks @KordingLab and

      Again, that's not what we're advocating. Lemme give a concrete example: the Sacramento, @somnirons and Senn model predicts that apical dendrite activity will decrease with error. That's a testable, falsifiable prediction.

      1 reply 0 retweets 2 likes
    3. Michael Hendricks‏ @MHendr1cks 30 Oct 2019
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      Replying to @tyrell_turing @KordingLab and

      I took that article to imply that we shouldn't care whether things like that are true or not. Just molecules?

      1 reply 0 retweets 0 likes
    4. Blake Richards‏ @tyrell_turing 30 Oct 2019
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      Replying to @MHendr1cks @KordingLab and

      No! With all due respect, if that's what you thought you completely missed the point. Our point was not: ignore cellular stuff. Our point was: don't try to explain computation cell-by-cell, bc computation emerges from evolutionary and learning optimization processes.

      1 reply 0 retweets 4 likes
    5. Blake Richards‏ @tyrell_turing 30 Oct 2019
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      Replying to @tyrell_turing @MHendr1cks and

      The argument is about how to study computation in the brain. It is not claiming that biology doesn't matter, quite the opposite actually.

      2 replies 0 retweets 2 likes
    6. Grace Lindsay‏ @neurograce 30 Oct 2019
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      Replying to @tyrell_turing @MHendr1cks and

      Yea, emphasizing that we should have our eye on learning rules that can actually lead to better performance in a large system doesn't mean having nothing to say about what that looks like on a cellular level. Learning rules are implemented through molecular mechanisms.

      3 replies 1 retweet 6 likes
    7. 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
    8. Blake Richards‏ @tyrell_turing 30 Oct 2019
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      Replying to @AdamMarblestone @neurograce and

      Totally agree!

      1 reply 1 retweet 1 like
    9. 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
    10. 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
      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.

      2:02 PM - 30 Oct 2019
      • 6 Likes
      • Dinos Meletis Tomas Fiers Jason Shepherd Blake Richards KordingLab 👨‍💻🧠∇🔬📈,🏋️‍♂️⛷️🏂🛹🕺⛰️☕🦖 Michael Hendricks
      2 replies 0 retweets 6 likes
        1. New conversation
        2. Adam Marblestone‏ @AdamMarblestone 30 Oct 2019
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          Replying to @AdamMarblestone @MHendr1cks and

          Ideally: highly multiplexed molecular imaging, including receptors, neuropeptides, ion channel distributions, etc, but with synaptic spatial resolution and in the context of at least sparse connectivity (linking synapses to their parent cells via barcodes) of the same cells.

          1 reply 0 retweets 2 likes
        3. Adam Marblestone‏ @AdamMarblestone 30 Oct 2019
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          Replying to @AdamMarblestone @MHendr1cks and

          Anyway, take this as one data point on a possible perspective: someone who thinks that's the technology we most need, but that Blake et al's theoretical framework may be in the direction of the theoretical framework we need...

          1 reply 0 retweets 2 likes
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        2. 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
        3. 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
        4. 5 more replies

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