Andrea Duggento

Redazione — Sat, 02 Sep 2023 11:44:32 +0000

Andrea Duggento

Professore Associato

PHYS-06/A - Fisica per le scienze della vita, l'ambiente e i beni culturali

duggento@med.uniroma2.it

Biografia

Andrea Duggento, professore Associato di Fisica Medica presso l’Università di Tor Vergata, ha conseguito la laurea e il master in Fisica teorica presso l’Università di Pisa e il dottorato di ricerca in Fisica presso la Lancaster University.

Laureato in Fisica Medica all’Università di Roma “Tor Vergata”, la ricerca di Andrea si concentra sui sistemi dinamici non lineari, sull’analisi statistica e sui processi nelle reti biologiche.

Il suo lavoro recente esplora le reti funzionali dirette nel cervello, con pubblicazioni su riviste prestigiose come Physical Review Letters e Philosophical Transactions of the Royal Society.

Profili

Scopus

Orcid

Google Scholar

Pubmed

Gomp

Teachings

Docenze

A. A. 2024 - 2025

A. A. 2023 - 2024

A. A. 2022 - 2023

Ultime 5 pubblicazioni (Scopus)

Spiking Reservoir Computing Architectures for Model-based Epileptic Brain State Recognition; Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS; 2025; DOI: 10.1109/EMBC58623.2025.11252881

Beyond multilayer perceptrons: Investigating complex topologies in neural networks; Neural Networks; March 2024; DOI: 10.1016/j.neunet.2023.12.012

4Ward: A relayering strategy for efficient training of arbitrarily complex directed acyclic graphs; Neurocomputing; 1 February 2024; DOI: 10.1016/j.neucom.2023.127058

Causal influence of brainstem response to transcutaneous vagus nerve stimulation on cardiovagal outflow; Brain Stimulation; 1 November 2023; DOI: 10.1016/j.brs.2023.10.007

VAESim: A probabilistic approach for self-supervised prototype discovery; Image and Vision Computing; September 2023; DOI: 10.1016/j.imavis.2023.104746

Ultime 5 pubblicazioni (PubMed)

Spiking Reservoir Computing Architectures for Model-based Epileptic Brain State Recognition
Bio-inspired networks offer rich dynamic capabilities with minimal energy demands, especially when implemented on neuromorphic hardware. In particular, the recurrence in brain circuits enables Recurrent Spiking Neural Networks (RSNNs) to generate complex spatio-temporal spike patterns, forming internal representations of time-varying signals. Despite this biological sophistication, such architectures are often applied to machine learning tasks with limited biological relevance. In this work, we...
Beyond multilayer perceptrons: Investigating complex topologies in neural networks
This study delves into the crucial aspect of network topology in artificial neural networks (NNs) and its impact on model performance. Addressing the need to comprehend how network structures influence learning capabilities, the research contrasts traditional multilayer perceptrons (MLPs) with models built on various complex topologies using novel network generation techniques. Drawing insights from synthetic datasets, the study reveals the remarkable accuracy of complex NNs, particularly in...
Causal influence of brainstem response to transcutaneous vagus nerve stimulation on cardiovagal outflow
CONCLUSION: Our causal approach allowed us to noninvasively evaluate directional interactions between fMRI BOLD signals from brainstem nuclei and cardiovagal outflow.
Heritability of human "directed" functional connectome
CONCLUSION: We suggest a robust role of heritability in influencing the directed connectivity of some cortico-subcortical circuits implicated in cognition. Further studies, for example using task-based fMRI and GC, are warranted to confirm the asymmetric effects of genetic factors on the functional connectivity within cognitive networks and their role in supporting executive functions and learning.
Can machine learning of post-procedural cone-beam CT images in acute ischemic stroke improve the detection of 24-h hemorrhagic transformation? A preliminary study
CONCLUSION: ML demonstrates high-sensitivity but low-accuracy 24-h HT prediction in AIS. The automated CB-CT imaging evaluation resizes sensitivity, specificity, and accuracy rates of visual interpretation reported in the literature so far. A standardized quantitative interpretation of CB-CT may be warranted to overcome the inter-operator variability.

Tecnica della riabilitazione psichiatrica 23/24 – Fisica Medica

Andrea Duggento

Andrea Duggento

Biografia

Profili

Scopus

Orcid

Google Scholar

Pubmed

Gomp

Teachings

Docenze

Ultime 5 pubblicazioni (Scopus)

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Ultime 5 pubblicazioni (PubMed)