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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-05022023-121539


Tipo di tesi
Tesi di dottorato di ricerca
Autore
LAGANI, GABRIELE
URN
etd-05022023-121539
Titolo
Bio-Inspired Approaches for Deep Learning: From Spiking Neural Networks to Hebbian Plasticity
Settore scientifico disciplinare
INF/01
Corso di studi
INFORMATICA
Relatori
tutor Prof. Amato, Giuseppe
correlatore Prof. Falchi, Fabrizio
correlatore Prof. Gennaro, Claudio
Parole chiave
  • bio-inspired
  • computer vision
  • deep learning
  • hebbian
  • neural networks
  • spiking
Data inizio appello
17/05/2023
Consultabilità
Non consultabile
Data di rilascio
17/05/2026
Riassunto
In the past few years, Deep Neural Network (DNN) architectures have achieved outstanding results in several Artificial Intelligence (AI) domains. Even though DNNs draw inspiration from biology, the training methods based on the backpropagation algorithm (\textit{backprop}) lack neuroscientific plausibility. The goal of this dissertation is to explore biologically-inspired solutions for the learning task. These are interesting because they can help to reproduce features of the human brain, for example, the ability to learn from a little experience. The investigation is divided into three phases: first, I explore a novel AI solution based on simulating neuronal biological cultures with a high level of detail, using biologically faithful Spiking Neural Network (SNN) models; second, I investigate neuroscientifically grounded \textit{Hebbian} learning rules, applied to traditional DNNs in combination with backprop, using computer vision as a case study; third, I consider a more applicative perspective, using neural features derived from Hebbian learning for multimedia content retrieval tasks. I validate the proposed methods on different benchmarks, including MNIST, CIFAR, and ImageNet, obtaining promising results, especially in learning scenarios with scarce data. Moreover, to the best of my knowledge, for the first time, I am able to bring bio-inspired Hebbian methods to ImageNet scale, consisting of over 1 million images.
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