137 citations · 288 across the 11 of their papers we have counts for
13 papers
Primate vision reveals a missing principle for robust dynamic AI
Matteo Dunnhofer, Christian Micheloni, Kohitij Kar
How does an intelligent visual system combine what objects look like with how they move while remaining robust as appearance changes? We addressed this question by comparing human…
AI-guided stimuli discovery and generation to optimize facial emotion perception studies in autism
Kushin Mukherjee, Na Yeon Kim, Maren Wehrheim +2
Understanding perceptual differences between autistic and neurotypical adults requires behavioral assays that are sensitive, reliable, and mechanistically informative. Facial emoti…
The macaque IT cortex but not current artificial vision networks encode object position in perceptually aligned coordinates
Elizaveta Yakubovskaya, Hamidreza Ramezanpour, Matteo Dunnhofer +1
Efficient interaction with the visual world requires not only accurate object identification but also precise localization of objects in space. While spatial ("where") processing h…
Modeling Dynamic Computations in the Primate Ventral Visual Stream
Matteo Dunnhofer, Maren Wehrheim, Hamidreza Ramezanpour +2
A major goal of computational neuroscience has been to explain how the primate ventral visual stream (VVS) transforms visual input into temporally evolving neural representations t…
Better, But Not Sufficient: Testing Video ANNs Against Macaque IT Dynamics
Matteo Dunnhofer, Christian Micheloni, Kohitij Kar
Feedforward artificial neural networks (ANNs) trained on static images remain the dominant models of the the primate ventral visual stream, yet they are intrinsically limited to st…
MAPS: Masked Attribution-based Probing of Strategies- A computational framework to align human and model explanations
Sabine Muzellec, Yousif Kashef Alghetaa, Simon Kornblith +1
Human core object recognition depends on the selective use of visual information, but the strategies guiding these choices are difficult to measure directly. We present MAPS (Maske…
RTify: Aligning Deep Neural Networks with Human Behavioral Decisions
Yu-Ang Cheng, Ivan Felipe Rodriguez, Sixuan Chen +3
Current neural network models of primate vision focus on replicating overall levels of behavioral accuracy, often neglecting perceptual decisions' rich, dynamic nature. Here, we in…
Low-cost, portable, easy-to-use kiosks to facilitate home-cage testing of non-human primates during vision-based behavioral tasks
Hamidreza Ramezanpour, Christopher Giverin, Kohitij Kar
Non-human primates (NHPs), especially rhesus macaques, have played a significant role in our current understanding of the neural computations underlying human vision. Apart from th…
How to optimize neuroscience data utilization and experiment design for advancing brain models of visual and linguistic cognition?
Greta Tuckute, Dawn Finzi, Eshed Margalit +8
In recent years, neuroscience has made significant progress in building large-scale artificial neural network (ANN) models of brain activity and behavior. However, there is no cons…
The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates
Kohitij Kar, James J DiCarlo
Visual object recognition -- the behavioral ability to rapidly and accurately categorize many visually encountered objects -- is core to primate cognition. This behavioral capabili…
Interpretability of artificial neural network models in artificial Intelligence vs. neuroscience
Kohitij Kar, Simon Kornblith, Evelina Fedorenko
Computationally explicit hypotheses of brain function derived from machine learning (ML)-based models have recently revolutionized neuroscience. Despite the unprecedented ability o…
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
Jonas Kubilius, Martin Schrimpf, Kohitij Kar +11
Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream. While initially…
Task-Driven Convolutional Recurrent Models of the Visual System
Aran Nayebi, Daniel Bear, Jonas Kubilius +5
Feed-forward convolutional neural networks (CNNs) are currently state-of-the-art for object classification tasks such as ImageNet. Further, they are quantitatively accurate models…