Lea Duncker

@leaduncker

PhD student at Gatsby Computational Neuroscience Unit

Vrijeme pridruživanja: listopad 2017.

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    3. pro 2019.

    Very excited to present our work on successor representations and state uncertainty as an oral next week at , Vancouver!

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  2. 26. stu 2019.
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    13. stu 2019.

    Want to join our team? We're hiring! Check out our great job openings and apply today

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    11. stu 2019.

    Neuro PDs of the world, are u a theorist in dream of running ur own experiment? an experimentalist wanting to falsify ur own theory? Apply to our unique + Postdoctoral Fellowship, for innovative ideas to bridge theory & experiment!

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    We have a unique Group Leader opportunity to work at the interface of Machine Learning and Neuroscience! We’re looking for an exciting and interdisciplinary researcher keen to contribute to an algorithmic understanding of the brain. More details:

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  7. 15. lis 2019.

    Excited to speak at the Minisymposium on ‘Artificial Intelligence and Neuroscience’. I’ll present a new single-trial neural population analysis method for quantifying contributions to inter-trial variability. Session 261, Monday Oct 21, 8.55-9.15am, Room S406A

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    Interested in doing a PhD with us? Applications for our 2020 PhD programme in Theoretical and Computational Neuroscience and Machine Learning are open! For more info see Deadline is the 17th of November.

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    15. ruj 2019.

    Can state-space models form a bridge between theory and data? and I lead a debate today at . My debate question is : ‘How closely do we need to constrain models with biological realism?’ What do you think? Come argue a position! 15:45 in H1012

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  11. 31. srp 2019.

    Nice explanation of latent dynamics, neural manifolds and some recent approaches for extracting such descriptions from data. Thanks for including us

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    20. lip 2019.

    The Gatsby Computational Neuroscience Unit is turning 21 this year – book your place at their public symposium -

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    26. svi 2019.

    ICML 2019 proceedings

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    2. tra 2019.

    A Visual Exploration of Gaussian Processes — A new Distill article by , Rebecca Kehlbeck, and

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    2. tra 2019.

    Sharing a Matlab toolkit for working with Neuropixel datasets and Kilosort2 sorts. Similar to cortex-lab's spikes repo, designed for careful preprocessing of SpikeGLX datasets without manually handling metadata. Documentation at

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    22. ožu 2019.

    Literally all of my notes from Cosyne19 - a lot of manifolds (neural and behavioral), but also a lot of embodiment, coding, and philosophy. Warning: it's really long, so if you want to go through the whole conference as me (but without all the natas) ...

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    28. velj 2019.

    Jorge Aurelio Menendez and are organizing a really fun workshop on Data, Dynamics, and Computation. I’ll speak about how we can use computational theory to build flexible yet interpretable models of neural activity. 7/n

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  18. 13. velj 2019.

    I'll be presenting more neuroscience-focused work relating to this at Poster II-102.

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  19. 13. velj 2019.

    We develop a new method to learn nonlinear dynamics in latent stochastic differential equations, directly conditioning a Gaussian Process prior of the dynamics on interpretable features like fixed points and local Jacobian matrices around them

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  20. 13. velj 2019.

    new preprint on arxiv: Learning interpretable continuous-time models of latent stochastic dynamical systems

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