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SUMMARY:Computational Neuroscience Journal Club - Yashar Ahmadian and Gido
  van de Ven
DTSTART:20211109T140000Z
DTEND:20211109T153000Z
UID:TALK165775@talks.cam.ac.uk
CONTACT:Jake Stroud
DESCRIPTION:Please join us for our fortnightly journal club online via zoo
 m where two presenters will jointly present a topic together. The next top
 ic is ‘Deep neural networks as models for the visual cortex’ presented
  by Yashar Ahmadian and Gido van de Ven.\n\nZoom information: https://us02
 web.zoom.us/j/84958321096?pwd=dFpsYnpJYWVNeHlJbEFKbW1OTzFiQT09 Meeting ID:
  841 9788 6178 Passcode: 659046\n\nSummary:\nIn recent years\, deep neural
  networks (DNNs) have enjoyed considerable success as computational models
  for the brain’s ventral visual stream. After a short introduction to th
 is field\, for which we follow the review by Yamins & DiCarlo (2016)\, we 
 discuss the paper by Bashivan et al. (2019) in part 1 of this journal club
 . This paper used a DNN\, trained on Imagenet in a supervised fashion\, as
  a model of the visual stream to synthesize images predicted to selectivel
 y activate certain neurons in the macaque visual cortex\, which they then 
 tested by presenting the synthesized images back to the macaque. In part 2
 \, we discuss the recent paper by Zhuang et al. (2021)\, which asks whethe
 r training DNNs in an unsupervised fashion\, rather than in the typical su
 pervised fashion\, can produce better models of the visual stream.\n\nRele
 vant reading:\n\nYamins\, D. L.\, & DiCarlo\, J. J. (2016). Using goal-dri
 ven deep learning models to understand sensory cortex. Nature neuroscience
 \, 19(3)\, 356. https://www.nature.com/articles/nn.4244\n\nBashivan\, P.\,
  Kar\, K.\, & DiCarlo\, J. J. (2019). Neural population control via deep i
 mage synthesis. Science\, 364(6439). https://science.sciencemag.org/conten
 t/364/6439/eaav9436.abstract\n\nZhuang\, C.\, Yan\, S.\, Nayebi\, A.\, Sch
 rimpf\, M.\, Frank\, M. C.\, DiCarlo\, J. J.\, & Yamins\, D. L. (2021). Un
 supervised neural network models of the ventral visual stream. Proceedings
  of the National Academy of Sciences\, 118(3). \nhttps://www.pnas.org/cont
 ent/118/3/e2014196118.short
LOCATION:Online on Zoom
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