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Ko and I discuss a range of topics around his work to understand our visual intelligence. Ko was a postdoc in James Dicarlo's lab, where he helped develop the convolutional neural network models that have become the standard for explaining core object recognition. He is starting his own lab at York University, where he will continue to expand and refine the models, adding important biological details and incorporating models for brain areas outside the ventral visual stream. He will also continue recording neural activity, and performing perturbation studies to better understand the networks involved in our visual cognition.



VISUAL INTELLIGENCE AND TECHNOLOGICAL ADVANCES LABTwitter: @KohitijKar.Related papersEvidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior.Neural population control via deep image synthesis.BI 075 Jim DiCarlo: Reverse Engineering Vision





0:00 - Intro
3:49 - Background
13:51 - Where are we in understanding vision?
19:46 - Benchmarks
21:21 - Falsifying models
23:19 - Modeling vs. experiment speed
29:26 - Simple vs complex models
35:34 - Dorsal visual stream and deep learning
44:10 - Modularity and brain area roles
50:58 - Chemogenetic perturbation, DREADDs
57:10 - Future lab vision, clinical applications
1:03:55 - Controlling visual neurons via image synthesis
1:12:14 - Is it enough to study nonhuman animals?
1:18:55 - Neuro/AI intersection
1:26:54 - What is intelligence?