Search Results for author:
Found 15 papers, 5 papers with code
Decoding visual brain representations from electroencephalography through Knowledge Distillation and latent diffusion models
Additionally, we incorporated an image reconstruction mechanism based on pre-trained latent diffusion models, which allowed us to generate an estimate of the images which had elicited EEG activity.
Through their eyes: multi-subject Brain Decoding with simple alignment techniques
We compared ridge regression, hyper alignment, and anatomical alignment techniques for fMRI data alignment.
Brain Captioning: Decoding human brain activity into images and text
Our brain captioning approach outperforms existing methods, while our image reconstruction pipeline generates plausible images with improved spatial relationships.
Beyond Multilayer Perceptrons: Investigating Complex Topologies in Neural Networks
Our study sheds light on the potential of complex topologies for enhancing the performance of ANNs and provides a foundation for future research exploring the interplay between multiple topological attributes and their impact on model performance.
Multimodal and multicontrast image fusion via deep generative models
As proof of concept, we test our architecture on the well characterized Human Connectome Project database demonstrating that our latent embeddings can be clustered into easily separable subject strata which, in turn, map to different phenotypical information which was not included in the embedding creation process.
DBGDGM: Dynamic Brain Graph Deep Generative Model
In this paper, we propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli
Brain decoding is a field of computational neuroscience that uses measurable brain activity to infer mental states or internal representations of perceptual inputs.
BayesNetCNN: incorporating uncertainty in neural networks for image-based classification tasks
The willingness to trust predictions formulated by automatic algorithms is key in a vast number of domains.
DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning
Graph neural networks (GNNs) have demonstrated success in learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data.
VAESim: A probabilistic approach for self-supervised prototype discovery
Then, we reconstruct the sample based on a similarity measure between the sample embedding and the prototypical vectors of the clusters.
Physically constrained neural networks to solve the inverse problem for neuron models
Systems biology and systems neurophysiology in particular have recently emerged as powerful tools for a number of key applications in the biomedical sciences.
Contrastive learning for unsupervised medical image clustering and reconstruction
The lack of large labeled medical imaging datasets, along with significant inter-individual variability compared to clinically established disease classes, poses significant challenges in exploiting medical imaging information in a precision medicine paradigm, where in principle dense patient-specific data can be employed to formulate individual predictions and/or stratify patients into finer-grained groups which may follow more homogeneous trajectories and therefore empower clinical trials.
4Ward: a Relayering Strategy for Efficient Training of Arbitrarily Complex Directed Acyclic Graphs
Thanks to their ease of implementation, multilayer perceptrons (MLPs) have become ubiquitous in deep learning applications.
An intertwined neural network model for EEG classification in brain-computer interfaces
In this paper, we present a deep neural network architecture specifically engineered to a) provide state-of-the-art performance in multiclass motor imagery classification and b) remain robust to preprocessing to enable real-time processing of raw data as it streams from EEG and BCI equipment.
Towards a predictive spatio-temporal representation of brain data
The characterisation of the brain as a "connectome", in which the connections are represented by correlational values across timeseries and as summary measures derived from graph theory analyses, has been very popular in the last years.
