Deep learning: the good, the bad, and the ugly

T Serre - Annual review of vision science, 2019 - annualreviews.org
Artificial vision has often been described as one of the key remaining challenges to be
solved before machines can act intelligently. Recent developments in a branch of machine …

Interpreting encoding and decoding models

N Kriegeskorte, PK Douglas - Current opinion in neurobiology, 2019 - Elsevier
Encoding and decoding models are widely used in systems, cognitive, and computational
neuroscience to make sense of brain-activity data. However, the interpretation of their results …

Limits to visual representational correspondence between convolutional neural networks and the human brain

Y Xu, M Vaziri-Pashkam - Nature communications, 2021 - nature.com
Convolutional neural networks (CNNs) are increasingly used to model human vision due to
their high object categorization capabilities and general correspondence with human brain …

Seeing it all: Convolutional network layers map the function of the human visual system

M Eickenberg, A Gramfort, G Varoquaux, B Thirion - NeuroImage, 2017 - Elsevier
Convolutional networks used for computer vision represent candidate models for the
computations performed in mammalian visual systems. We use them as a detailed model of …

Neural encoding and decoding with deep learning for dynamic natural vision

H Wen, J Shi, Y Zhang, KH Lu, J Cao, Z Liu - Cerebral cortex, 2018 - academic.oup.com
Convolutional neural network (CNN) driven by image recognition has been shown to be
able to explain cortical responses to static pictures at ventral-stream areas. Here, we further …

Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis

J Diedrichsen, N Kriegeskorte - PLoS computational biology, 2017 - journals.plos.org
Representational models specify how activity patterns in populations of neurons (or, more
generally, in multivariate brain-activity measurements) relate to sensory stimuli, motor …

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

R Rajalingham, EB Issa, P Bashivan, K Kar… - Journal of …, 2018 - Soc Neuroscience
Primates, including humans, can typically recognize objects in visual images at a glance
despite naturally occurring identity-preserving image transformations (eg, changes in …

Deep neural networks in computational neuroscience

TC Kietzmann, P McClure, N Kriegeskorte - BioRxiv, 2017 - biorxiv.org
The goal of computational neuroscience is to find mechanistic explanations of how the
nervous system processes information to support cognitive function and behaviour. At the …

[HTML][HTML] A large and rich EEG dataset for modeling human visual object recognition

AT Gifford, K Dwivedi, G Roig, RM Cichy - NeuroImage, 2022 - Elsevier
The human brain achieves visual object recognition through multiple stages of linear and
nonlinear transformations operating at a millisecond scale. To predict and explain these …

Visual number sense in untrained deep neural networks

G Kim, J Jang, S Baek, M Song, SB Paik - Science advances, 2021 - science.org
Number sense, the ability to estimate numerosity, is observed in naïve animals, but how this
cognitive function emerges in the brain remains unclear. Here, using an artificial deep …