Kris Wu

@kris_neuro

PhD student in computational neuroscience . Formerly .

Vrijeme pridruživanja: srpanj 2019.

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  1. Prikvačeni tweet
    2. lis 2019.

    Excited to share our work! It is a great honour to work on this project and be involved in such a great collaboration.

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  2. proslijedio/la je Tweet
    17. sij

    Mechanisms underlying the response of mouse cortical networks to optogenetic manipulation

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  3. proslijedio/la je Tweet
    14. sij

    Applications now open for the courses in : 👉 Quantitative approaches to (19 July - 8 August 2020) 👉 Computational (10 - 28 August 2020) ❗️To apply: - deadline for both courses: 16 March 2020

    , , i još njih 7
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  4. proslijedio/la je Tweet
    6. pro 2019.

    Work classifies firing rate nonlinearities in recurrent spiking and rate networks (SSN,+extnded to Ricciardi rate models) Both types hv param regimes defn'd by connectivity, and spiking net analysis seems to rule out some commonly used rate-model regimes.

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  5. proslijedio/la je Tweet
    9. sij

    This is a great resource for identifying speakers from underrepresented groups: . Check it out! You might find someone in your field doing exciting work that you didn't know about. You can also send them nominations (yourself or a colleague).

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  6. proslijedio/la je Tweet
    9. pro 2019.

    I'm happy to share my comments on the climate for men from my talk:

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  7. proslijedio/la je Tweet

    Thrilled to share a new paper from my group on contrast sensitivity in mouse V1. Dan Millman led this study, showing that VIP neurons selectively enhance weak but behaviorally-relevant stimuli in superficial cortex.

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  8. proslijedio/la je Tweet
    15. stu 2019.

    Excited to share a new preprint by students Vicky Zhu and Cody Baker Excitatory-inhibitory balance is ubiquitous in cortical circuits. How does this balance shape neural representations, manifolds, and computations?

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  9. 18. stu 2019.
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  10. 14. stu 2019.

    What is left? Curiosity. Anything else? Mathematics.

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  11. proslijedio/la je Tweet
    11. stu 2019.
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  12. proslijedio/la je Tweet

    New preprint with and Nicolas Brunel on storing random sequences in recurrent networks. Sequences maintain robust decoding but display highly labile dynamics to changes in connectivity, similar to recent observations in hippocampus and parietal cortex.

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  13. proslijedio/la je Tweet
    24. lis 2019.

    Our dense connectome from mouse cortex is out in Science - 7 years of work, now the largest mammalian connectome. Thanks to a great team of students!!

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  14. proslijedio/la je Tweet
    22. lis 2019.

    I could really use your help in this regard: I was wondering whether anyone has information regarding Iranian passport holders being able to attend ? or any other year in the past during Trump's presidency? many thanks to those who can share or ask around.

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  15. proslijedio/la je Tweet
    21. lis 2019.

    Happy to present the latest co-production of the Ferraguti & Lüthi labs with - VIP interneurons indeed are "very important players" for learning!

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  16. proslijedio/la je Tweet
    20. lis 2019.

    Timelapse of IBL behavior/ephys rig build... made them both in less than a day!

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  17. proslijedio/la je Tweet

    Representation of Distance and Direction of Nearby Boundaries in Retrosplenial Cortex

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  18. proslijedio/la je Tweet
    17. lis 2019.

    Today we introduce two large spike-based data sets for the quantitative comparison of spiking neural network architectures and neuromorphic hardware. Creative Commons License. Data: Paper:

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  19. proslijedio/la je Tweet
    7. lis 2019.

    My first from the is live! In short: we used CaMPARI2 to label neurons based on in vivo activity in L4 of visual cortex, found that intrinsic excitability contributes more strongly than E/I ratio to differences in mean activity levels. Feedback welcome!

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  20. proslijedio/la je Tweet
    6. lis 2019.

    "Stable memory with unstable synapses" Susman et al. 2019 "memories stored as time-varying attractors... are more resilient to erosion than fixed-points ...can be learned by biologically plausible learning-rules and support associative retrieval"

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  21. proslijedio/la je Tweet
    7. lis 2019.

    Finally out ! Low-dimensional representations in the forebrain: predict behavior and are modified through learning. Involves plasticity of inhibitory interactions. More at: . Great time , now starting !

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