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“I hear & receive all this feedback!” replied who responded with explanations but also promises to fix certain issues and clarify others in any write-ups about the study. It’s really weird to see different autistic traits ascribed to different “support levels” (cont.) /boY7nZmiA7 The issue with “functioning levels” is that they’re not static and linear, not just the wordsī. They went into greater detail in a string of tweets, bringing up such issues as the ranges available in response to a question asking when an autism diagnosis was received, tweeting, “I’d argue there’s a lot more difference between getting diagnosed at 3 vs 17 then there is between 75 and also pointed out issues with the responses available for the survey question asking respondents about support level, tweeting, “It’s really weird to see different autistic traits ascribed to different ‘support levels.’”Ī. Really interesting research area, but very much not a fan of the way some of the “demographic” questions about autism are set up “Really interesting research area, but very much not a fan of the way some of the ‘demographic’ questions about autism are set up,” Twitter user replied. If nothing else, the logical thing to do would be to study it more.” << YES! See flyer below to participate in #cannabis #research in #autism /JmGautT7Nj “Marijuana may or may not be helpful to autistic young people, although autistic adults who use it frequently have positive things to say. For proof, we offer up one such conversation sparked by Twitter user assistant professor of psychology at the University of New Orleans in Louisiana, after she tweeted out information about participating in a survey on cannabis use in autism. This may sound like a far-out concept, but back-and-forths on Twitter can be peaceful, polite and productive. We also share the winning submissions: three teams used transformers (Perceiver, GPT, and BERT), one used Pointnet. Super excited to share the dataset + evaluators from this year’s Multi-Agent Behavior Challenge on unsupervised + self-supervised representations of behavior! Our dataset consists of mouse (9 mil frames) and fly (4 mil frames) social interactions for studying behavioral representation learning!Īnn Kennedy, assistant professor of neuroscience at Northwestern University in Chicago, Illinois, who contributed to the dataset and was featured in a Spectrum profile article this week, shared her excitement in a quote tweet. We are excited to release the dataset from the 2022 MABe Challenge! ?
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The dataset “consists of mouse (9 mil frames) and fly (4 mil frames) social interactions for studying behavioral representation learning!” tweeted Jennifer Sun, a graduate student at the California Institute of Technology in Pasadena. The team in question announced the release of a new dataset “from real-world behavioral neuroscience experiments.” Other fly-modeling work had Twitter buzzing, too - and this one also included mice. There were many other tweets we don’t have room to share, but they had one thing in common: big excitement about this work and its implications for the future of neuroscience. This is such an impressive experimental and computational paper, and it underscores the tractability of the fly visual system. ĭan O’Shea, a neuroscientist at Stanford University in California, was also excited about the paper, highlighting that “it underscores the tractability of the fly visual system.”
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Here, biological and artificial knockouts predict neural activity and distributed function in complex brain areas. I am dazzled,” tweeted Cori Bargmann, a neuroscientist at Rockefeller University in New York City.Ĭlassical genetics uses knockouts to infer functional relationships. “Here, biological and artificial knockouts predict neural activity and distributed function in complex brain areas. (yes, that is indeed a fictive female fly, good guess!) /PSkexLJldZ Yet, they are unable to tell us which artificial neuron directly corresponds to a biological neuron… until now!
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His results showed “that visual projection neurons at the interface between the eye and brain form a distributed population code that collectively sculpts social behavior.”ĭeep nets are great at predicting visual neurons. Yet, they are unable to tell us which artificial neuron directly corresponds to a biological neuron… until now!” Cowley wrote. “Deep nets are great at predicting visual neurons. Up first was a new preprint shared by Benjamin Cowley, a computational neuroscientist at Princeton University, in which he and his colleagues used a deep neural network, or a ‘deep net,’ to model the visual system of a fruit fly. Twitter can sometimes feel like a zoo, and this week, two behavior-modeling studies had our feeds wild with excitement.