Slides

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
[pdf, powerpoint]
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
[powerpoint, pdf]
Recurrent computations for pattern completion. 2016-20-10, Harvard Systems Club
[pdf]