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Found 7 papers, 7 papers with code
Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs
Reconstructing perceived natural images from fMRI signals is one of the most engaging topics of neural decoding research.
Multimodal neural networks better explain multivoxel patterns in the hippocampus
The human hippocampus possesses "concept cells", neurons that fire when presented with stimuli belonging to a specific concept, regardless of the modality.
On the role of feedback in visual processing: a predictive coding perspective
That is, we let the optimization process determine whether top-down connections and predictive coding dynamics are functionally beneficial.
Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics
The reconstruction errors are used to iteratively update the network's representations across timesteps, and to optimize the network's feedback weights over the natural image dataset-a form of unsupervised training.
Does language help generalization in vision models?
Vision models trained on multimodal datasets can benefit from the wide availability of large image-caption datasets.
Predictive coding feedback results in perceived illusory contours in a recurrent neural network
Finally we validated our conclusions in a deeper network (VGG): adding the same predictive coding feedback dynamics again leads to the perception of illusory contours.
Brain-inspired predictive coding dynamics improve the robustness of deep neural networks
The reconstruction errors are used to iteratively update the network’s representations across timesteps, and to optimize the network's feedback weights over the natural image dataset--a form of unsupervised training.
