activity
20202026
most citedCrowding Reveals Fundamental Differences in Local vs. Global Processing in Humans and Machines

42 citations · 108 across the 6 of their papers we have counts for

collaborators

6 papers

q-bio.NC2026

What biology can, and cannot, tell us about conscious AI

Ulysse Klatzmann, Adrien Doerig

Progress in AI is turning machine consciousness from a philosophical curiosity into a societal issue, and has led to criticism of the widespread computational functionalism framewo…

q-bio.NC2023★ 4 cited

End-to-end topographic networks as models of cortical map formation and human visual behaviour: moving beyond convolutions

Zejin Lu, Adrien Doerig, Victoria Bosch +4

Computational models are an essential tool for understanding the origin and functions of the topographic organisation of the primate visual system. Yet, vision is most commonly mod…

cs.CV2023

Characterising representation dynamics in recurrent neural networks for object recognition

Sushrut Thorat, Adrien Doerig, Tim C. Kietzmann

Recurrent neural networks (RNNs) have yielded promising results for both recognizing objects in challenging conditions and modeling aspects of primate vision. However, the represen…

q-bio.NC2022★ 24 cited

The neuroconnectionist research programme

Adrien Doerig, Rowan Sommers, Katja Seeliger +8

Artificial Neural Networks (ANNs) inspired by biology are beginning to be widely used to model behavioral and neural data, an approach we call neuroconnectionism. ANNs have been la…

cs.CV2022★ 38 cited

Visual representations in the human brain are aligned with large language models

Adrien Doerig, Tim C Kietzmann, Emily Allen +4

The human brain extracts complex information from visual inputs, including objects, their spatial and semantic interrelations, and their interactions with the environment. However,…

q-bio.NC2020★ 42 cited

Crowding Reveals Fundamental Differences in Local vs. Global Processing in Humans and Machines

Adrien Doerig, Alban Bornet, Oh-Hyeon Choung +1

Feedforward Convolutional Neural Networks (ffCNNs) have become state-of-the-art models both in computer vision and neuroscience. However, human-like performance of ffCNNs does not…