Performance-optimized hierarchical models predict neural responses in higher visual cortex

DLK Yamins, H Hong, CF Cadieu… - Proceedings of the …, 2014 - National Acad Sciences
The ventral visual stream underlies key human visual object recognition abilities. However,
neural encoding in the higher areas of the ventral stream remains poorly understood. Here …

Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream

U Güçlü, MAJ Van Gerven - Journal of Neuroscience, 2015 - Soc Neuroscience
Converging evidence suggests that the primate ventral visual pathway encodes increasingly
complex stimulus features in downstream areas. We quantitatively show that there indeed …

Deep neural networks rival the representation of primate IT cortex for core visual object recognition

CF Cadieu, H Hong, DLK Yamins, N Pinto… - PLoS computational …, 2014 - journals.plos.org
The primate visual system achieves remarkable visual object recognition performance even
in brief presentations, and under changes to object exemplar, geometric transformations …

Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance

NJ Majaj, H Hong, EA Solomon… - Journal of …, 2015 - Soc Neuroscience
To go beyond qualitative models of the biological substrate of object recognition, we ask:
can a single ventral stream neuronal linking hypothesis quantitatively account for core object …

[图书][B] Visual cortex and deep networks: learning invariant representations

TA Poggio, F Anselmi - 2016 - books.google.com
A mathematical framework that describes learning of invariant representations in the ventral
stream, offering both theoretical development and applications. The ventral visual stream is …

Recurrence is required to capture the representational dynamics of the human visual system

TC Kietzmann, CJ Spoerer… - Proceedings of the …, 2019 - National Acad Sciences
The human visual system is an intricate network of brain regions that enables us to
recognize the world around us. Despite its abundant lateral and feedback connections …

Deep supervised, but not unsupervised, models may explain IT cortical representation

SM Khaligh-Razavi, N Kriegeskorte - PLoS computational biology, 2014 - journals.plos.org
Inferior temporal (IT) cortex in human and nonhuman primates serves visual object
recognition. Computational object-vision models, although continually improving, do not yet …

Hierarchical modular optimization of convolutional networks achieves representations similar to macaque IT and human ventral stream

DL Yamins, H Hong, C Cadieu… - Advances in neural …, 2013 - proceedings.neurips.cc
Humans recognize visually-presented objects rapidly and accurately. To understand this
ability, we seek to construct models of the ventral stream, the series of cortical areas thought …

Unsupervised neural network models of the ventral visual stream

C Zhuang, S Yan, A Nayebi… - Proceedings of the …, 2021 - National Acad Sciences
Deep neural networks currently provide the best quantitative models of the response
patterns of neurons throughout the primate ventral visual stream. However, such networks …

Brain-like object recognition with high-performing shallow recurrent ANNs

J Kubilius, M Schrimpf, K Kar… - Advances in neural …, 2019 - proceedings.neurips.cc
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 …