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5/ To explain and unify these diverse numerical results, we also developed a mathematical theory, based on the theory of pattern formation, that explains when and why grid structure emerges, and what lattice (amorphous, square or hexagonal) would dominate under diff. conditionspic.twitter.com/c39XQY05Xh
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1/ New paper at
#NeurIPS2019: A unified theory for the origin of grid cells through the lens of pattern formation: https://papers.nips.cc/paper/9191-a-unified-theory-for-the-origin-of-grid-cells-through-the-lens-of-pattern-formation … lead by Ben Sorscher, Gabriel Mel and Sam Ocko. Spotlight: Thu Dec 12th 10:35 -- 10:40 AM @ West Exhibition Hall C + B3pic.twitter.com/Kjw47gRnlQ
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Our paper lead by
@ItsNeuronal on "Discovering Precise Temporal Patterns in Large-Scale Neural Recordings" now published@NeuroCellPress : https://www.cell.com/neuron/fulltext/S0896-6273(19)30894-3 … learn how to turn the spike trains on the left into those on the right just by reordering the rows.pic.twitter.com/JAJ13jjaMN
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Looking forward to visiting the Institute for Advanced Study (
@the_IAS) this week to speak at a workshop on the Theory of Deep Learning: https://www.math.ias.edu/wtdl Already had great scientific discussions while walking through the Institute Woods!pic.twitter.com/bxjtcnxsYC
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Officially got tenure in Applied Physics
@Stanford! This feat is really due to a brilliant, creative, and fun group of students and postdocs I have had the great fortune of working with over the years. And thanks to amazing colleagues and mentors at Stanford and beyond!pic.twitter.com/LYKnEeawNG
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4/ a mean field analysis suggests a critically excitable operating regime for V1 in which large assemblies of order N similarly tuned neurons can be recruited by stimulating a tiny subset of only order square root of N neurons, thereby enabling strikingly high sensitivitypic.twitter.com/7SljzHErJm
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3/ theoretical analysis reveals this very low threshold, for the ignition of both large cell assemblies and perception, is almost as low as it can possibly be while still optimally avoiding false positive percepts driven by fluctuations in spontaneous activitypic.twitter.com/TvuqYzSmgj
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2/ that visual cortex operates in a highly sensitive critically excitable regime in which stimulating a tiny subset of ~20 cells with similar orientation tuning is sufficient to both selectively recruit a large fraction of similarly responding cells and drive a specific perceptpic.twitter.com/Jf1qoIyiOz
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1/ New in
@sciencemagazine w/@KarlDeisseroth lab: https://science.sciencemag.org/content/early/2019/07/17/science.aaw5202 …: new opsin + multi-photon holography to image ~4000 cells in 3D volumes over 5 cortical layers while also stimulating ~50 neurons to directly drive visual percepts; data analysis and theory reveal…pic.twitter.com/Wy1PLvrzz6
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Lenka
@zdeborova gives a great view in one slide on what it will take to get to a unified theory of#deeplearning at the Theoretical Physics for Deep Learning workshop at#ICML19 : https://sites.google.com/view/icml2019phys4dl …pic.twitter.com/FUOg5jM2YG
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New from lab! Unsupervised algorithm to reveal spike-time patterns buried in data. i.e. it can take the left rasters and discover a hidden oscillation by just reordering them. code available! paper: https://www.biorxiv.org/content/10.1101/661165v1 … thread: https://twitter.com/ItsNeuronal/status/1137034488800636928 … Congrats
@ItsNeuronal!pic.twitter.com/lzAsizfeTd
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Congrats
@kiahhardcastle on an awesome#neuroscience PhD defense! She's also known as Kiah of house Hard Castle, first of her name, Queen of the pyramidals, stellates and the First Brains, breaker of PhD chains, mother of spike trains, the rightful ruler of 52 Brodmann regions...pic.twitter.com/VvXd9YjM4w
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3/ Our theoretically derived scaling laws, which assume electrode activity is a noisy random projection of single units, are verified in neural data, and reveal that in many experiments, we can skip spike-sorting and still obtain relatively accurate neural pop dynamicspic.twitter.com/Zm9wxb3q5R
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2/ We connect theory to experiment by deriving scaling laws relating the complexity of neural dynamics, # of neurons, # of electrodes, and the geometric distortion of neural pop dynamics incurred, by simply skipping spike-sorting, and working directly with electrode activitypic.twitter.com/QRiF49J7fw
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1/ New
#neuroscience paper@NeuroCellPress: Accurately estimating neural population dynamics without spike sorting https://www.cell.com/neuron/fulltext/S0896-6273(19)30428-3 … Congrats@EricMTrautmann,@sergeydoestweet and@Deephype and many members of@shenoystanford lab! Also first@neuropixels data in primates!pic.twitter.com/P0RGMizjX9
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#KatieBouman speaking@Stanford on black holes! Amazing: in 100 years humanity derived black holes from pure mathematics, then turned the whole earth into a telescope to see one 53 million light years away, and figured out it weighs 6.5 billion suns! brings a tear to my eye...pic.twitter.com/EsUeFgdyJt
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An exciting day of talks today
@Stanford spanning#AI#neuroscience,#psychology,#economics,#education#politicalscience and more as part of the opening symposium of the@StanfordHAI Human Centered AI Institute. You can see the talks live streamed at: https://www.youtube.com/watch?v=yjSlatKnfqQ …https://twitter.com/StanfordHAI/status/1107627992816275456 …
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I took the red pill. Lets see how deep the rabbit hole of
@SussilloDavid's twitter feed goes!pic.twitter.com/pzSxXjzt46
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Oh interesting - I did not realize there was a @davidsussillo who would grow up to be
@SussilloDavid. In this case I have to agree with@chetan here - in the end...pic.twitter.com/BE7ebLbE7V
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My new post for the
@Stanford Human Centered AI Initiative: a personal vision of how#neuroscience#psychology#ai#physics#mathematics and other fields can work together to both understand biological intelligence and create artificial intelligence! https://hai.stanford.edu/news/the_intertwined_quest_for_understanding_biological_intelligence_and_creating_artificial_intelligence/ …pic.twitter.com/Uc6T9uu5xx
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