Dottorando
gian.angelini@hotmail.com
Gianfrancesco Angelini ha conseguito una laurea in Ingegneria Chimica presso l’Università della Calabria e un master in Neuroingegneria e Bio-ICT presso l’Università di Genova.
Ha svolto stage e corsi di formazione in intelligenza artificiale e ha lavorato come Data Scientist, Data Engineer, DevOps e Python Software Developer.
Nel 2022 è entrato a far parte del Programma Nazionale di Dottorato in IA, concentrandosi sulle reti neurali di seconda e terza generazione ed esplorando autonomous agents, sentient agents, e knowledge extraction da big data.
CONCLUSIONS: These results not only validate our method's accuracy and reliability but also establish a foundation for a streamlined, non-invasive approach to dynamic PET data quantification. By offering a precise and less invasive alternative to traditional quantification methods, our technique holds significant promise for expanding the applicability of PET imaging across a wider range of tracers, thereby enhancing its utility in both clinical research and diagnostic settings.
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 innovative method that employs knowledge distillation to train an EEG classifier and reconstruct images from the ImageNet and THINGS-EEG 2 datasets using only electroencephalography (EEG) data from participants who have viewed the images themselves (i.e. "brain decoding"). We analyzed EEG recordings from 6...
CONCLUSIONS: The promotion of a Mediterranean diet during pregnancy has a significant effect on maternal brain structure.
Objective.Brain decoding is a field of computational neuroscience that aims to infer mental states or internal representations of perceptual inputs from measurable brain activity. This study proposes a novel approach to brain decoding that relies on semantic and contextual similarity.Approach.We use several functional magnetic resonance imaging (fMRI) datasets of natural images as stimuli and create a deep learning decoding pipeline inspired by the bottom-up and top-down processes in human...
Sex differences affect Parkinson's disease (PD) development and manifestation. Yet, current PD identification and treatments underuse these distinctions. Sex-focused PD literature often prioritizes prevalence rates over feature importance analysis. However, underlying aspects could make a feature significant for predicting PD, despite its score. Interactions between features require consideration, as do distinctions between scoring disparities and actual feature importance. For instance, a...