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Prikvačeni tweet
Thus, we think between-subject prediction is a neat analysis approach that suggests an underlying regularity in how different places are mapped in the rodent hippocampus. Preprint with
@jeremyRmanning and@mattmizumi: https://www.biorxiv.org/content/10.1101/2020.01.27.922062v1 ….Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Surprisingly, this between-subject prediction worked better than the within-subject controls we tried, and simulations suggest simple explanations such as correlated firing rates between A and B can be ruled out. 3/
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We adapted a technique from human fMRI work (hyperalignment, inspired by
@haxbylab) enabling us to use how subject 1 encodes A and B (e.g. left and right arms of a maze), and how subject 2 encodes A, to predict how subject 2 encodes B. 2/Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Where hippocampal place cells have their fields is famously hard to predict: if you know how a given subject encodes location or environment A, that doesn't tell you much about how it encodes B. 1/
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Hung-Tu Chen proslijedio/la je Tweet
A common model explaining flexible decision making, grid fields and cognitive control https://biorxiv.org/cgi/content/short/856849v1 …
#biorxiv_neursciHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Hung-Tu Chen proslijedio/la je Tweet
The present is only meaningful with respect to the past and future. Super work!https://twitter.com/biorxiv_neursci/status/1194707765903708160 …
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Hung-Tu Chen proslijedio/la je Tweet
These two papers from
@naoshigeuchida and@gershbrain et al. are truly beautiful. A great example of how theory and experiment can enhance each other with all contributions properly acknowledged. https://www.biorxiv.org/content/10.1101/803437v1 …https://www.biorxiv.org/content/10.1101/805366v1 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Hung-Tu Chen proslijedio/la je Tweet
Excited to share that I’m teaching a *new course* on multi-task & meta-learning! Topics incl. optimization-based meta-learning, lifelong learning, meta-RL , etc Slides & assignments being posted. Lecture videos to be publicly released after the course. http://cs330.stanford.edu pic.twitter.com/Gmve2kUvHW
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Hung-Tu Chen proslijedio/la je Tweet
Hippocampal Remapping as Hidden State Inference https://www.biorxiv.org/content/10.1101/743260v1 … joint work with
@honisanders and Matt WilsonHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Hung-Tu Chen proslijedio/la je Tweet
Our newest! "Anxiety, avoidance, and sequential evaluation" Computational psychiatry project with
@szorowi1 (1st) &@nathanieldaw. When agent's predictions about outcomes of actions are pessimistic ==> avoidance, aversive pruning, freezing behavior. https://www.biorxiv.org/content/10.1101/724492v1 …pic.twitter.com/NrYoeoTxIs
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Hung-Tu Chen proslijedio/la je Tweet
I'm updating the syllabus for my "Debugging the brain" class. What do you think are the most important computational psychiatry papers of the last few years?
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Hung-Tu Chen proslijedio/la je Tweet
Super thrilled to share our years of work on human replay with the world, now out in Cell
@CellCellPress, https://www.cell.com/cell/fulltext/S0092-8674(19)30640-3 … with my amazing supervisors:@behrenstimb@zebkDotCom and Ray Dolan.@WCHN_UCL@MPC_CompPsych@OxfordWIN@DeepMindAI (1/16)Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Hung-Tu Chen proslijedio/la je Tweet
We are looking for lab techs, grad students and postdocs to join our collaborative BRAIN initiative project on the neural basis of directional orientation and decision-making! With Kathy Cullen, Jim Knierim, Jeff Taube & Kechen Zhang, featuring behavior, ephys/imaging & models.
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Hung-Tu Chen proslijedio/la je Tweet
Everyone should be watching this TED talk from her in 2016. It is so clear and full of the wonders of doing science. https://www.youtube.com/watch?v=BIvezCVcsYs …https://twitter.com/MIT_CSAIL/status/1116020858282180609 …
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Hung-Tu Chen proslijedio/la je Tweet
This is a really beautiful paper. It is exactly what theory should be. A clear concise formal argument to explain a wealth of seemingly disparate data. Very pretty
@nathanieldaw@marcelomattarhttps://twitter.com/NatureNeuro/status/1054399508002603016 …
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Hung-Tu Chen proslijedio/la je Tweet
A natural history of dopamine; a thread on
@fluketc paper https://goo.gl/yLnoJr : Dopamine is critical for novel learning. Pioneering work developed methods to record from dopamine neurons. However, recordings come from animals re-learning, rather than learning anew. Why?pic.twitter.com/Td0QCTLXDd
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Hung-Tu Chen proslijedio/la je Tweet
Question for neuro Twitter: what's the best place/way to release a large behavioral dataset? anything similar to Neurodata Without Borders, or
@OpenNeuroOrg? We'd like to make a HUGE dataset of rat and human Parametric Working Memory publicly available, including learning period!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Hung-Tu Chen proslijedio/la je Tweet
Excited to share our new preprint with Marc Howard, "Predicting the future with multi-scale successor representations". Mathy, but we've tried to give intuitive explanations of equations. Would love to hear thoughts! https://www.biorxiv.org/content/early/2018/10/22/449470 …pic.twitter.com/sqX0W6okR8
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Hung-Tu Chen proslijedio/la je Tweet
Our preprint reporting a paradoxical relationship between motivational shifts and hippocampal replay content is now available! We found that when rats were hungry, replay was biased toward the water arm of a T-maze; when thirsty, toward the food arm (https://www.biorxiv.org/content/early/2018/08/22/397950 …).
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