13 citations · 22 across the 20 of their papers we have counts for
21 papers
PCDT: A Predictive Cognitive Digital Twin Framework for Intelligent and Autonomous 6G Network Ecosystems
John Sengendo, Andreas Kassler, Fabrizio Granelli
Future 6G networks are expected to operate as intelligent and autonomous ecosystems where monitoring, prediction, and control are integrated into continuous self-optimization loops…
Retrieval-Based Brain Decoding by Alignment, not Complexity
Matteo Ciferri, Matteo Ferrante, Nicola Toschi
A prominent theory in cognitive science suggests that concepts in the brain are organized as high-dimensional vectors, with semantic meaning captured by directions and relative ang…
Mapping Whisper Representations to Human ECoG Responses with Interpretable Time-Resolved Neural Encoding
Matteo Ciferri, Tommaso Boccato, Michal Olak +2
Understanding how speech foundation models relate to human cortical activity is a key challenge for computational neuroscience. Here, we investigate how internal representations fr…
Seeing the imagined: latent functional alignment in visual imagery decoding from fMRI data
Fabrizio Spera, Tommaso Boccato, Michal Olak +5
Recent progress in visual brain decoding from fMRI has been enabled by large-scale datasets such as the Natural Scenes Dataset (NSD) and powerful diffusion-based generative models.…
Simple Models, Rich Representations: Visual Decoding from Primate Intracortical Neural Signals
Matteo Ciferri, Matteo Ferrante, Nicola Toschi
Understanding how neural activity gives rise to perception is a central challenge in neuroscience. We address the problem of decoding visual information from high-density intracort…
Transforming Multimodal Models into Action Models for Radiotherapy
Matteo Ferrante, Alessandra Carosi, Rolando Maria D Angelillo +1
Radiotherapy is a crucial cancer treatment that demands precise planning to balance tumor eradication and preservation of healthy tissue. Traditional treatment planning (TP) is ite…
Bridging Auditory Perception and Language Comprehension through MEG-Driven Encoding Models
Matteo Ciferri, Matteo Ferrante, Nicola Toschi
Understanding the neural mechanisms behind auditory and linguistic processing is key to advancing cognitive neuroscience. In this study, we use Magnetoencephalography (MEG) data to…
Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion
Matteo Ferrante, Tommaso Boccato, Grigorii Rashkov +1
This paper presents a novel approach towards creating a foundational model for aligning neural data and visual stimuli across multimodal representationsof brain activity by leverag…
R&B -- Rhythm and Brain: Cross-subject Decoding of Music from Human Brain Activity
Matteo Ferrante, Matteo Ciferri, Nicola Toschi
Music is a universal phenomenon that profoundly influences human experiences across cultures. This study investigates whether music can be decoded from human brain activity measure…
A Differentiable Model for Optimizing the Genetic Drivers of Synaptogenesis
Tommaso Boccato, Matteo Ferrante, Nicola Toschi
There is growing consensus among neuroscientists that neural circuits critical for survival are the result of genomic decompression processes. We introduce SynaptoGen, a novel comp…
Decoding visual brain representations from electroencephalography through Knowledge Distillation and latent diffusion models
Matteo Ferrante, Tommaso Boccato, Stefano Bargione +1
Decoding visual representations from human brain activity has emerged as a thriving research domain, particularly in the context of brain-computer interfaces. Our study presents an…
Through their eyes: multi-subject Brain Decoding with simple alignment techniques
Matteo Ferrante, Tommaso Boccato, Nicola Toschi
Previous brain decoding research primarily involves single-subject studies, reconstructing stimuli via fMRI activity from the same subject. Our study aims to introduce a generaliza…
Brain Captioning: Decoding human brain activity into images and text
Matteo Ferrante, Furkan Ozcelik, Tommaso Boccato +2
Every day, the human brain processes an immense volume of visual information, relying on intricate neural mechanisms to perceive and interpret these stimuli. Recent breakthroughs i…
Beyond Multilayer Perceptrons: Investigating Complex Topologies in Neural Networks
Tommaso Boccato, Matteo Ferrante, Andrea Duggento +1
In this study, we explore the impact of network topology on the approximation capabilities of artificial neural networks (ANNs), with a particular focus on complex topologies. We p…
Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli
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. Therefore, we p…
BayesNetCNN: incorporating uncertainty in neural networks for image-based classification tasks
Matteo Ferrante, Tommaso Boccato, Nicola Toschi
The willingness to trust predictions formulated by automatic algorithms is key in a vast number of domains. However, a vast number of deep architectures are only able to formulate…
VAESim: A probabilistic approach for self-supervised prototype discovery
Matteo Ferrante, Tommaso Boccato, Simeon Spasov +2
In medicine, curated image datasets often employ discrete labels to describe what is known to be a continuous spectrum of healthy to pathological conditions, such as e.g. the Alzhe…
Application of the nnU-Net for automatic segmentation of lung lesion on CT images, and implication on radiomic models
Matteo Ferrante, Lisa Rinaldi, Francesca Botta +17
Lesion segmentation is a crucial step of the radiomic workflow. Manual segmentation requires long execution time and is prone to variability, impairing the realisation of radiomic…
Contrastive learning for unsupervised medical image clustering and reconstruction
Matteo Ferrante, Tommaso Boccato, Simeon Spasov +2
The lack of large labeled medical imaging datasets, along with significant inter-individual variability compared to clinically established disease classes, poses significant challe…
Physically constrained neural networks to solve the inverse problem for neuron models
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. Nevertheless, such mo…
4Ward: a Relayering Strategy for Efficient Training of Arbitrarily Complex Directed Acyclic Graphs
Tommaso Boccato, Matteo Ferrante, Andrea Duggento +1
Thanks to their ease of implementation, multilayer perceptrons (MLPs) have become ubiquitous in deep learning applications. The graph underlying an MLP is indeed multipartite, i.e.…