Slides

  • The neural architecture of language: Integrative modeling converges on predictive processing. 2021-03-09, MIT BCS Saxe Lab
    [pdf]
  • Program for Software Engineering, Guest Lecture: Engineering an Artificial Biological Intelligence. 2021-01-15, UNA/TUM, LMU
    [pdf]
  • CBMM Panel Discussion: Should models of cortex be falsifiable? 2020-12-07, MIT (with Tomaso Poggio, Gabriel Kreiman, Josh McDermott, Leyla Isik, Susan Epstein, Jenelle Feather)
    [video]
  • The neural architecture of language: Integrative modeling converges on predictive processing. 2020-12-01, France NLP GDR TAL
    [pdf]
  • Integrative Benchmarking to Advance Neural Models of Vision and Language. 2020-11-25, Virtual Computational Neuroscience Journal Club
    [pdf, recording]
  • Integrative Benchmarking and the Value of Quantitative Models. 2020-10-19, CCN GAC linear/nonlinear workshop
    [pdf]
  • Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream. 2020-10-06, C-BRIC Annual Review
    [pdf]
  • Artificial Neural Networks Accurately Predict Language Processing in the Brain. 2020-08-18, MIT BCS Cog Lunch
    [pdf, code]
  • Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 2020-03-31, neuromatch.io contributed talk
    [pdf, video (starts at 22:45)]
  • Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 2019-12-12, NeurIPS Oral
    [talk recording, slides pdf, slides powerpoint, poster pdf, code, 3min-video]
  • Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 2019-10-29, MIT BCS Cog Lunch
    [pdf]
  • Deep Networks and Pytorch. 2019-08-21, Harvard-MIT Computational Neuroscience Journal Club
    [notebook, github]
  • Continual Learning with Self-Organizing Maps. 2018-12-07, NeurIPS Continual Learning Workshop
    [poster pdf]
  • Aligning Deep Neural Networks and Visual Cortex. 2018-03-01, MIT IQ Launch
    [pdfpowerpoint]
  • A Flexible Approach to Automated RNN Architecture Generation. 2018-02-14, Tenenbaum lab
    [pdf, powerpoint]
  • Introduction to Deep Learning for Neuroscientists. 2017-10-27, MIT BCS Peer Lectures
    [powerpointpdf]
  • Recurrent computations for pattern completion. 2016-20-10, Harvard Systems Club
    [pdf]