2018
DOI: 10.1101/408385
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CORnet: Modeling the Neural Mechanisms of Core Object Recognition

Abstract: Deep artificial neural networks with spatially repeated processing (a.k.a., deep convolutional ANNs) have been established as the best class of candidate models of visual processing in primate ventral visual processing stream. Over the past five years, these ANNs have evolved from a simple feedforward eight-layer architecture in AlexNet to extremely deep and branching NAS-Net architectures, demonstrating increasingly better object categorization performance and increasingly better explanatory power of both neu… Show more

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Cited by 156 publications
(201 citation statements)
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References 40 publications
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“…To further map out the space of possible architectures and a baseline of neural, behavioral, and performance scores, we included an in-house-developed family of models with up to moderate ImageNet performance, termed BaseNets: lightweight AlexNet-like architectures with six convolutional layers and a single fully-connected layer, captured at various stages of training. Various hyperparameters were varied between BaseNets, such as the number of filter maps, nonlinearities, pooling, learning rate scheduling, and so on, and formed a basis for the CORnet family of models (Kubilius et al, 2018b). We also tested CORnet-S, a new model that was developed with the goal of rivaling the best models on Brain-Score while being significantly shallower than competitors by leveraging bottleneck architecture and recurrence (Kubilius et al, 2018b).…”
Section: Candidate Modelsmentioning
confidence: 99%
See 1 more Smart Citation
“…To further map out the space of possible architectures and a baseline of neural, behavioral, and performance scores, we included an in-house-developed family of models with up to moderate ImageNet performance, termed BaseNets: lightweight AlexNet-like architectures with six convolutional layers and a single fully-connected layer, captured at various stages of training. Various hyperparameters were varied between BaseNets, such as the number of filter maps, nonlinearities, pooling, learning rate scheduling, and so on, and formed a basis for the CORnet family of models (Kubilius et al, 2018b). We also tested CORnet-S, a new model that was developed with the goal of rivaling the best models on Brain-Score while being significantly shallower than competitors by leveraging bottleneck architecture and recurrence (Kubilius et al, 2018b).…”
Section: Candidate Modelsmentioning
confidence: 99%
“…Various hyperparameters were varied between BaseNets, such as the number of filter maps, nonlinearities, pooling, learning rate scheduling, and so on, and formed a basis for the CORnet family of models (Kubilius et al, 2018b). We also tested CORnet-S, a new model that was developed with the goal of rivaling the best models on Brain-Score while being significantly shallower than competitors by leveraging bottleneck architecture and recurrence (Kubilius et al, 2018b). CORnet-S is composed of four recurrent areas with two to three convolutions each and a fully-connected layer at the end.…”
Section: Candidate Modelsmentioning
confidence: 99%
“…Among the first five models that have high IT predictivity and are common for the two datasets is CORnet-S ( Figure 3). CORnets (7) have an architecture that approximates the number and size of visual areas in the macaque brain. CORnet-S is a recurrent ANN with skip connections.…”
Section: Resultsmentioning
confidence: 99%
“…In fact, deep convolutional neural networks have emerged as successful models of the ventral stream (Yamins et al 2014), and authors investigating the limitations of purely feedforward architectures within this family have proposed including temporal dynamics and adaptive mechanisms (Vinken et al 2019), or recurrent computations (Kar et al 2019;Kietzmann et al 2019;Tang et al 2018). Indeed, it has been suggested that convolutional networks that excel in object recognition need to be very deep simply to approximate operations that could be implemented more efficiently by recurrent architectures (Kar et al 2019;Kubilius et al 2018;Liao and Poggio 2016). These theoretical developments point to the potential importance of intrinsic dynamics for the cortical representation of visual stimuli.…”
Section: Discussionmentioning
confidence: 99%