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Prikvačeni tweet
"The Human Brain encodes a Chronicle of Visual Events at each Instant of Time", by
@valentinwyart and I: the tl;dr thread: https://www.biorxiv.org/content/10.1101/846576v1 …pic.twitter.com/iySGP52al9Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Jean-Rémi King proslijedio/la je Tweet
Interactive visualization of brain activity with
@mne_python is getting an update thanks to https://github.com/GuillaumeFavelier … and https://docs.pyvista.org/#EEG#MEG#neuroscience#visualization@mne_news (to appear soon in the next MNE release and still WIP)pic.twitter.com/H4Wd0SuokLHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Jean-Rémi King proslijedio/la je Tweet
If you are an academic and you travel to conferences etc please take a moment to fill this in - we are very interested in what climate-related policies would attract most support
https://twitter.com/Rachel_Bedder/status/1202503253101031424 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Jean-Rémi King proslijedio/la je Tweet
Pour tous ceux qui s'intéressent aux travaux de notre Conseil Scientifique, ce livre "La science au service de l'école" (coédition Canopé/Odile Jacob) passe en revue les actions du CSEN depuis près de deux ans: lecture, évaluations, métacognition, etc https://www.odilejacob.fr/catalogue/sciences-humaines/education-enseignement-pedagogie/science-au-service-de-l-ecole_9782738148650.php … https://twitter.com/nvallejog/status/1201435662643318784 …pic.twitter.com/pWUgvwsdNb
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Jean-Rémi King proslijedio/la je Tweet
Applications for the Facebook AI Residency program are open. US (NYC, Seattle, Menlo Park): https://www.facebook.com/careers/jobs/672572899933116/ … UK (London): https://www.facebook.com/careers/jobs/799952110461505/ … Deadline: 2020-01-31 https://www.facebook.com/careers/jobs/672572899933116/ …
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Jean-Rémi King proslijedio/la je Tweet
Our work on 'Distributed coding of choice, action, and engagement across the mouse brain' is published today in Nature! With
@peterzh,@MatteoCarandini, and@kennethd_harris. Here's a quick recap and what's new since bioRxiv.https://www.nature.com/articles/s41586-019-1787-x …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
(Put the cell in markdown to go back to default.)
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Jupyter: improve your notebook workflow with a simple trick. I often get asked, so twitter might find it useful. 1/2pic.twitter.com/5iBQouv4Gd
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Jean-Rémi King proslijedio/la je Tweet
New preprint
https://www.biorxiv.org/content/10.1101/856336v1.article-info … a) complementary role of MEG & fMRI for prediction and brain-behavior association b) MEG highlights source power in beta-alpha range c) multimodal prediction always pays off even with missing data #MEG#EEG#MachineLearning#python#rstatspic.twitter.com/eNaXpZ7SFx
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14/14 In the meantime, you can find our preprint here! https://www.biorxiv.org/content/10.1101/846576v1 …pic.twitter.com/aP4JwQyrc2
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13/14 ... Confirming and generalizing these results will thus require (many) more studies.
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12/14 A note on the limits of this study: i) it is based on scalp EEG: the underlying neuronal mechanisms thus remain unknown (e.g. adaptation, lateral inhibition etc); ii) we neither used naturalistic images nor did we vary presentation rate and subjects' task...
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11/14 In sum, we demonstrate that ~4 successive images can be simultaneously decoded from brain activity. A particular hierarchical network specifically accounts for this computational property, and thus allows the brain to continuously make use of multiple snapshots of the pastpic.twitter.com/d2dGGqzjxC
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10/14 We originally did not expect low-level features (i.e. stim orientation) to propagate in the hierarchy. However, post-hoc analyses showed that this phenomenon is also observed in deep conv networks (yes, we probably had bad expectations...)pic.twitter.com/x7mQ17fALH
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9/14 Localizing each decoder confirmed that visual representations sequentially propagated across the multiple levels of the visual pathwaypic.twitter.com/W8kRA9oqoQ
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8/14 This architecture allows successive images to be simultaneously represented within distinct processing stages.pic.twitter.com/Y6RxBRcBv1
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7/14 After simulating >1 billion models, only 2 architectures qualitatively explain the EEG dynamics of visual representations. Both suggest that visual information i) propagates in the network when the image changes and ii) is otherwise maintained by an activity-silent mechanismpic.twitter.com/ofvhIYjwHt
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6/14 Each architecture leads to specific dynamics, here characterized with Temporal Generalization (TG, see King
@StanDehaene 2014,@StokesNeuro, Myers and colleagues 2013 for more details)pic.twitter.com/2eS1IhlEM2Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
5/14 A variety of neural architectures can explain this phenomena. We use a simple dynamical system modeling framework to tease them apart.pic.twitter.com/KdjaoxIxHK
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4/14 Question: how long are visual contents and visual flows represented in brain activity? Answer: ~1 sec, i.e. 4x longer than the stimulus duration. Surprising consequence: *successive* visual features can be *simultaneously* decoded at each time instant.pic.twitter.com/uZhCUXbROw
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