13 citations · 21 across the 17 of their papers we have counts for
19 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…
From Neurons to Conversation: Speech Brain-Computer Interfaces
Moein Khajehnejad, Forough Habibollahi, Tommaso Boccato +4
Speech brain-computer interfaces (BCIs) aim to restore communication by transforming neural activity related to speech, language, or communicative intent into external outputs such…
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.…
Decoding the decoder: Contextual sequence-to-sequence modeling for intracortical speech decoding
Michal Olak, Tommaso Boccato, Matteo Ferrante
Speech brain--computer interfaces require decoders that translate intracortical activity into linguistic output while remaining robust to limited data and day-to-day variability. W…
BrainWhisperer: Leveraging Large-Scale ASR Models for Neural Speech Decoding
Tommaso Boccato, Michal Olak, Matteo Ferrante
Decoding continuous speech from intracortical recordings is a central challenge for brain-computer interfaces (BCIs), with transformative potential for individuals with conditions…
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…
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…
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…
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…
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.…