Search Results for author: Nicola Toschi

Found 15 papers, 5 papers with code

Decoding visual brain representations from electroencephalography through Knowledge Distillation and latent diffusion models

no code implementations8 Sep 2023 Matteo Ferrante, Tommaso Boccato, Stefano Bargione, Nicola Toschi

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.

Brain Decoding EEG +3

Through their eyes: multi-subject Brain Decoding with simple alignment techniques

no code implementations1 Aug 2023 Matteo Ferrante, Tommaso Boccato, Nicola Toschi

We compared ridge regression, hyper alignment, and anatomical alignment techniques for fMRI data alignment.

Brain Decoding regression

Brain Captioning: Decoding human brain activity into images and text

no code implementations19 May 2023 Matteo Ferrante, Furkan Ozcelik, Tommaso Boccato, Rufin VanRullen, Nicola Toschi

Our brain captioning approach outperforms existing methods, while our image reconstruction pipeline generates plausible images with improved spatial relationships.

Brain Decoding Depth Estimation +3

Beyond Multilayer Perceptrons: Investigating Complex Topologies in Neural Networks

no code implementations31 Mar 2023 Tommaso Boccato, Matteo Ferrante, Andrea Duggento, Nicola Toschi

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

no code implementations28 Mar 2023 Giovanna Maria Dimitri, Simeon Spasov, Andrea Duggento, Luca Passamonti, Pietro Li`o, Nicola Toschi

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.

Dimensionality Reduction

DBGDGM: Dynamic Brain Graph Deep Generative Model

no code implementations26 Jan 2023 Alexander Campbell, Simeon Spasov, Nicola Toschi, Pietro Lio

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.

Dynamic Link Prediction Graph Classification +1

Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli

1 code implementation13 Dec 2022 Matteo Ferrante, Tommaso Boccato, Nicola Toschi

Brain decoding is a field of computational neuroscience that uses measurable brain activity to infer mental states or internal representations of perceptual inputs.

Brain Decoding Image Reconstruction

BayesNetCNN: incorporating uncertainty in neural networks for image-based classification tasks

no code implementations27 Sep 2022 Matteo Ferrante, Tommaso Boccato, Nicola Toschi

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

2 code implementations27 Sep 2022 Alexander Campbell, Antonio Giuliano Zippo, Luca Passamonti, Nicola Toschi, Pietro Lio

Graph neural networks (GNNs) have demonstrated success in learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data.

Graph structure learning

VAESim: A probabilistic approach for self-supervised prototype discovery

1 code implementation25 Sep 2022 Matteo Ferrante, Tommaso Boccato, Simeon Spasov, Andrea Duggento, Nicola Toschi

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

no code implementations24 Sep 2022 Matteo Ferrante, Andera Duggento, Nicola Toschi

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

no code implementations24 Sep 2022 Matteo Ferrante, Tommaso Boccato, Simeon Spasov, Andrea Duggento, Nicola Toschi

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.

Clustering Contrastive Learning +3

4Ward: a Relayering Strategy for Efficient Training of Arbitrarily Complex Directed Acyclic Graphs

1 code implementation5 Sep 2022 Tommaso Boccato, Matteo Ferrante, Andrea Duggento, Nicola Toschi

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

no code implementations4 Aug 2022 Andrea Duggento, Mario De Lorenzo, Stefano Bargione, Allegra Conti, Vincenzo Catrambone, Gaetano Valenza, Nicola Toschi

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.

Denoising EEG +1

Towards a predictive spatio-temporal representation of brain data

1 code implementation29 Feb 2020 Tiago Azevedo, Luca Passamonti, Pietro Liò, Nicola Toschi

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.

Binary Classification

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