15 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…
Hierarchical Pooling for Sheaf Neural Networks
Dionisia Naddeo, Carlo Abate, Pietro Liò +2
Sheaf Neural Networks (SNNs) generalize Graph Neural Networks (GNNs) by replacing scalar node signals with stalk-valued signals and by using restriction maps to measure compatibili…
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…
Training Neural Networks by Optimizing Neuron Positions
Laura Erb, Tommaso Boccato, Alexandru Vasilache +2
The high computational complexity and increasing parameter counts of deep neural networks pose significant challenges for deployment in resource-constrained environments, such as e…
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…
Genetic Motifs as a Blueprint for Mismatch-Tolerant Neuromorphic Computing
Tommaso Boccato, Dmitrii Zendrikov, Nicola Toschi +1
Mixed-signal implementations of SNNs offer a promising solution to edge computing applications that require low-power and compact embedded processing systems. However, device misma…
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…