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The neuroconnectionist research programme
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 …
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …
Functional neuroimaging as a catalyst for integrated neuroscience
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 …
behaving human brain. By tracking whole-brain signals across a diverse range of cognitive …
High-resolution image reconstruction with latent diffusion models from human brain activity
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 …
understand how the brain represents the world, and to interpret the connection between …
Onellm: One framework to align all modalities with language
Multimodal large language models (MLLMs) have gained significant attention due to their
strong multimodal understanding capability. However existing works rely heavily on modality …
strong multimodal understanding capability. However existing works rely heavily on modality …
Frontostriatal salience network expansion in individuals in depression
Decades of neuroimaging studies have shown modest differences in brain structure and
connectivity in depression, hindering mechanistic insights or the identification of risk factors …
connectivity in depression, hindering mechanistic insights or the identification of risk factors …
Reconstructing the mind's eye: fmri-to-image with contrastive learning and diffusion priors
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 …
images from brain activity. Our model comprises two parallel submodules that are …
On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
Natural scene reconstruction from fMRI signals using generative latent diffusion
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 …
perceived natural images based on fMRI signals. Previous studies have succeeded in re …
Getting aligned on representational alignment
Biological and artificial information processing systems form representations that they can
use to categorize, reason, plan, navigate, and make decisions. How can we measure the …
use to categorize, reason, plan, navigate, and make decisions. How can we measure the …
Driving and suppressing the human language network using large language models
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 …
human brain responses to language. Here, using functional-MRI-measured brain responses …