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

By Michael Eickenberg, Alexandre Gramfort, Gaël Varoquaux, Bertrand Thirion. 2017

376 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

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Citations in the corpus

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy. (2018, positive): “…hopes that such models would yield biological insights (Lehky and Sejnowski, 1988; Zipser and Andersen, 1988), contemporary deep neural networks have been shown to replicate key aspects of visual system organization (Eickenberg et al., 2017; G€uçl€u and van Gerven, 2015; Yamins and DiCarlo, 2016).”
Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision (2016, methodology): “…were previously explored with low-level visual features (Kay et al. 2008; Nishimoto et al. 2011) or high-level semantic features (Huth et al. 2012, 2016a), and more recently with hierarchical features extracted by the CNN from static pictures (Güçlü and van Gerven 2015a; Eickenberg et al. 2016).”
Cognitive computational neuroscience (2018, background): “As we move gradually along the ventral visual stream, higher layers of the neural networks gradually come to provide a better basis for explaining the representations.(122,123,124) Higher layers of deep convolutional neural networks also resemble the IT cortical representation in that both enable th”
Neural network models and deep learning - a primer for biologists (2019, background): “do not explain all of the explainable variance in neuronal responses. (Cadieu et al., 2014; Khaligh-Razavi and Kriegeskorte, 2014; Yamins et al., 2014; Güçlü and van Gerven, 2015; Cichy et al., 2016; Eickenberg et al., 2017; Wen et al., 2017; Nayebi et al., 2018), and yet multiple functional incompa”
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future (2020, result): “This relationship between models and the brain wherein later layers in the network better predict higher areas of the ventral stream has been found in several other studies, including using human fMRI [14], MEG [15], and with video instead of static images as the stimuli [16].”