Drew Linsley

@DrewLinsley

Computational neuroscience @ brown. Friend to all dogs.

Providence, RI
Vrijeme pridruživanja: ožujak 2009.

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  1. proslijedio/la je Tweet
    3. velj

    This really messed with my head for a few minutes before I realised

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  2. proslijedio/la je Tweet

    So thrilled to be teaching this class again! Come write your novels w/ me!

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  3. proslijedio/la je Tweet

    A minimal Turing test (McCoy & Ullman, 2018): “Say one word that convinces us you're human.”

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  4. proslijedio/la je Tweet
    22. sij

    My blogpost on how & why we use convolutional neural networks as a model of the visual system is probably the most read thing I've ever written and it's now been expanded & updated into a proper review article, complete with 136 references & 5 new figures!

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  5. proslijedio/la je Tweet
    20. sij

    I am very excited, to finally share this: Using a functional approach we uncovered a mechanism for whitening, a fundamental computation that decorrelates neuronal population activity for pattern classification by memory networks.

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  6. proslijedio/la je Tweet
    16. sij

    A new microscope technique shows cells’ fine structure in new detail. The method, developed by Eric Betzig, Harald Hess + colleagues, combines 3D super-resolution fluorescence microscopy and electron microscopy in whole cells. Published today in Science!

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  7. proslijedio/la je Tweet
    18. sij

    The petition for SFN to reduce emissions has reopened. 1180 signatures so far. This is a tiny fraction of the number of members. Please consider signing. This is a no brainer.

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  9. proslijedio/la je Tweet
    10. sij

    New preprint from the lab: "Individual differences among deep neural network models." Work with , , and Courtney Spoerer. below. 1/7

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  10. proslijedio/la je Tweet
    9. sij

    What are the mechanisms that modulate gain in the ? Check out this latest Review by and

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  11. proslijedio/la je Tweet
    7. sij

    New paper out! We provide evidence that feedforward convnets (ffCNNs) cannot implement human-like global computations because of their *architecture*, and not merely because of the way they are *trained*.

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  12. proslijedio/la je Tweet
    6. sij

    *REMINDER + PLS RT* Our workshop, From Neuroscience to Artificially Intelligent Systems (NAISys), has an abstract deadline of January 10. This Friday!!! But, it's only 1-page, so easy-peasy: Please send in ideas for how neuroscience can inform AI!

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  13. proslijedio/la je Tweet
    2. sij

    also - very nice (+ impressively coordinated) thread by the authors here

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  14. proslijedio/la je Tweet
    26. pro 2019.

    Some really fascinating results linking functional characterizations of optically recorded synaptic inputs and EM reconstructions of the synapses to challenge theories of Hebbian learning's role in shaping selectivity in Ferret V1

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  15. 23. pro 2019.

    In sum: (Paper 1) Introduce a deep feedback model exhibits an orientation-tilt illusion and outperforms state-of-the-art vision models in contour detection. (Paper 2) Test the computational role of feedback connections for solving complex visual tasks. See you at ICLR! 17/17

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  16. 23. pro 2019.

    Our feedback models are also significantly better than leading feedforward DNNs at capturing human decisions on these tasks. The recurrent visual strategies learned by the models are a better fit for human decision making than visual strategies learned by typical DNNs. 16/17

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  17. 23. pro 2019.

    We train deep feedback models on these tasks, and find a double-dissociation. Horizontal connections are important for leveraging Gestalt, or in this case path tracing. Top-down connections are important for leveraging semantic information, and selecting letters. 15/17

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  18. 23. pro 2019.

    The first dataset is solved with "Gestalt": finding a dot and tracing to the other end of the long path. The second dataset is solved with semantics: recognizing one of the two letters, ignoring the other, and counting the dots on it. 14/17

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  19. 23. pro 2019.

    Feedback connections in visual cortex have broadly been split into local "horizontal" connections and long-range "top-down" connections. We created two datasets to disentangle these connections, both of which ask whether two dots are on the same or different objects. 13/17

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  20. 23. pro 2019.

    In our other paper, , we explore a long-standing question in vision science: What are the computational roles of different forms of feedback connections in visual cortex? 12/17

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