Matteo Ferrante

PhD Student

matteo.ferrante@uniroma2.it

Biography

Matteo Ferrante is a Ph.D. candidate at the National AI Ph.D. – Health and Life Sciences program, studying neuromorphic architectures and generative therapeutic “telepathy.”

He has degrees in Physics and Biomedical Physics from the University of Pavia. His interests lie in artificial intelligence, precision medicine, and neuroscience.

His Ph.D. project focuses on decoding visual stimuli in the brain and generating activation maps using mappings between latent spaces of the brain and artificial neural networks.

Profiles

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Last 5 articles (Scopus)

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  • Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging; Computer Methods and Programs in Biomedicine; November 2024; DOI: 10.1016/j.cmpb.2024.108375
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  • Retrieving and reconstructing conceptually similar images from fMRI with latent diffusion models and a neuro-inspired brain decoding model; Journal of Neural Engineering; 1 August 2024; DOI: 10.1088/1741-2552/ad593c
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  • Decoding visual brain representations from electroencephalography through knowledge distillation and latent diffusion models; Computers in Biology and Medicine; August 2024; DOI: 10.1016/j.compbiomed.2024.108701
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  • Beyond multilayer perceptrons: Investigating complex topologies in neural networks; Neural Networks; March 2024; DOI: 10.1016/j.neunet.2023.12.012
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  • 4Ward: A relayering strategy for efficient training of arbitrarily complex directed acyclic graphs; Neurocomputing; 1 February 2024; DOI: 10.1016/j.neucom.2023.127058
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Last 5 articles (PubMed)

  • Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging

    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 brain representations from electroencephalography through knowledge distillation and latent diffusion models

    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...

  • Retrieving and reconstructing conceptually similar images from fMRI with latent diffusion models and a neuro-inspired brain decoding model

    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...

  • Enabling uncertainty estimation in neural networks through weight perturbation for improved Alzheimer's disease classification

    CONCLUSION: We believe that being able to estimate the uncertainty of a prediction, along with tools that can modulate the behavior of the network to a degree of confidence that the user is informed about (and comfortable with), can represent a crucial step in the direction of user compliance and easier integration of deep learning tools into everyday tasks currently performed by human operators.

  • Effects of transcranial magnetic stimulation on reactive response inhibition

    Reactive response inhibition cancels impending actions to enable adaptive behavior in ever-changing environments and has wide neuropsychiatric implications. A canonical paradigm to measure the covert inhibition latency is the stop-signal task (SST). To probe the cortico-subcortical network underlying motor inhibition, transcranial magnetic stimulation (TMS) has been applied over central nodes to modulate SST performance, especially to the right inferior frontal cortex and the presupplementary...

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