Search Results for author:
Found 5 papers, 2 papers with code
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.
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task.
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream
In this work, we construct models of the ventral stream using a novel optimization procedure for category-level object recognition problems, and produce RDMs resembling both macaque IT and human ventral stream.
