Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake,
behaving human brain. By tracking whole-brain signals across a diverse range of cognitive …

Reconstructing visual experiences from human brain activity offers a unique way to
understand how the brain represents the world, and to interpret the connection between …

Decades of neuroimaging studies have shown modest differences in brain structure and
connectivity in depression, hindering mechanistic insights or the identification of risk factors …

We present MindEye, a novel fMRI-to-image approach to retrieve and reconstruct viewed
images from brain activity. Our model comprises two parallel submodules that are …

Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …

Multimodal large language models (MLLMs) have gained significant attention due to their
strong multimodal understanding capability. However existing works rely heavily on modality …

In neural decoding research, one of the most intriguing topics is the reconstruction of
perceived natural images based on fMRI signals. Previous studies have succeeded in re …

Transformer models such as GPT generate human-like language and are predictive of
human brain responses to language. Here, using functional-MRI-measured brain responses …

Advances in artificial intelligence have inspired a paradigm shift in human neuroscience,
yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide …