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Found 5 papers, 4 papers with code
Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment
Our study presents FFA as a promising proof-of-concept for the mechanisms underlying how feedback connections in the visual cortex support flexible visual functions.
Brain-like representational straightening of natural movies in robust feedforward neural networks
Prior work established straightening in neural representations of the primate primary visual cortex (V1) and perceptual straightening in human behavior as a hallmark of biological vision in contrast to artificial feedforward neural networks which did not demonstrate this phenomenon as they were not explicitly optimized to produce temporally predictable movie representations.
Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?
We therefore developed Brain-Score – a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brain’s mechanisms for core object recognition – and we deployed it to evaluate a wide range of state-of-the-art deep ANNs.
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream.
Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures
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 the primate ventral visual processing stream.
