News

Factorized visual representations in the primate visual system and deep neural networks

https://www.biorxiv.org/content/10.1101/2020.10.19.345561v1

Representational straightening of natural movies in robust feedforward neural networks

https://www.abstractsonline.com/pp8/#!/10619/presentation/71603

Marmosets can robustly recognize faces defined by 3D geometry alone, invariant to pose, lighting & background changes

https://www.abstractsonline.com/pp8/#!/10619/presentation/64828

Marmosets can rapidly and robustly recognize faces invariant to pose, lighting & background changes
Beyond invariant object classification: new normative principles for models of IT visual cortex
Symbiotic Learning of Dual Discrimination and Reconstruction Networks

https://www.youtube.com/watch?v=Vc2AL8i8rqc

Conserved core visual object recognition across simian primates: Marmoset image-by-image behavior mirrors that of humans and macaques

https://www.biorxiv.org/content/10.1101/2020.10.19.345561v1

Core visual object recognition behavior in common marmosets

https://www.abstractsonline.com/pp8/#!/7883/presentation/69168

Weight Tracking as a Tool to Predict Parturition in the Common Marmoset (Callithrix jacchus)

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6774462/

Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?

https://www.biorxiv.org/content/10.1101/407007v1

Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior

https://www.biorxiv.org/content/10.1101/354753v1

Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals

https://www.biorxiv.org/content/10.1101/092551v2.article-info

Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks

https://www.biorxiv.org/content/10.1101/240614v4

Evidence that the ventral stream codes the errors used in hierarchical inference and learning

https://www.biorxiv.org/content/10.1101/092551v1?versioned=true

Columbia Affiliations
Department of Neuroscience
Zuckerman Mind Brain Behavior Institute