Hebbian Learning Meets Deep Convolutional Neural Networks. - Giuseppe Amato 001; Fabio Carrara; Fabrizio Falchi; Claudio Gennaro; Gabriele Lagani (ICIAP (1), 2019)

Training Convolutional Neural Networks With Hebbian Principal Component Analysis. - Gabriele Lagani; Giuseppe Amato 001; Fabrizio Falchi; Claudio Gennaro (CoRR, 2020)

Training Convolutional Neural Networks with Competitive Hebbian Learning Approaches. - Gabriele Lagani; Fabrizio Falchi; Claudio Gennaro; Giuseppe Amato 001 (LOD, 2021)

Evaluating Hebbian Learning in a Semi-supervised Setting. - Gabriele Lagani; Fabrizio Falchi; Claudio Gennaro; Giuseppe Amato 001 (LOD, 2021)

Assessing Pattern Recognition Performance of Neuronal Cultures through Accurate Simulation. - Gabriele Lagani; Raffaele Mazziotti; Fabrizio Falchi; Claudio Gennaro; Guido Marco Cicchini; Tommaso Pizzorusso; Federico Cremisi; Giuseppe Amato 001 (NER, 2021)

Hebbian semi-supervised learning in a sample efficiency setting. - Gabriele Lagani; Fabrizio Falchi; Claudio Gennaro; Giuseppe Amato 001 (Neural Networks, 2021)

Deep Features for CBIR with Scarce Data using Hebbian Learning. - Gabriele Lagani; Davide Bacciu; Claudio Gallicchio; Fabrizio Falchi; Claudio Gennaro; Giuseppe Amato 001 (CBMI, 2022)

Recent Advancements on Bio-Inspired Hebbian Learning for Deep Neural Networks. - Gabriele Lagani (SEBD, 2022)

FastHebb: Scaling Hebbian Training of Deep Neural Networks to ImageNet Level. - Gabriele Lagani; Claudio Gennaro; Hannes Fassold; Giuseppe Amato 001 (SISAP, 2022)

Comparing the performance of Hebbian against backpropagation learning using convolutional neural networks. - Gabriele Lagani; Fabrizio Falchi; Claudio Gennaro; Giuseppe Amato 001 (Neural Comput. Appl., 2022)

AIMH Lab for a Susteinable Bio-Inspired AI. - Gabriele Lagani; Fabrizio Falchi; Claudio Gennaro; Giuseppe Amato 001 (Ital-IA, 2023)

Scaling Bio-Inspired Neural Features to Real-World Image Retrieval Problems. - Gabriele Lagani (SEBD, 2023)

Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey. - Gabriele Lagani; Fabrizio Falchi; Claudio Gennaro; Giuseppe Amato 001 (CoRR, 2023)

Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey. - Gabriele Lagani; Fabrizio Falchi; Claudio Gennaro; Giuseppe Amato 001 (CoRR, 2023)

A Biologically-Inspired Approach to Biomedical Image Segmentation. - Luca Ciampi; Gabriele Lagani; Giuseppe Amato 001; Fabrizio Falchi (ECCV Workshops (14), 2024)

CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edge. - Gabriele Lagani; Fabrizio Falchi; Claudio Gennaro; Giuseppe Amato 001 (ECCV Workshops (11), 2024)

Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imaging. - Luca Ciampi; Gabriele Lagani; Giuseppe Amato 001; Fabrizio Falchi (CoRR, 2024)

Scalable bio-inspired training of Deep Neural Networks with FastHebb. - Gabriele Lagani; Fabrizio Falchi; Claudio Gennaro; Hannes Fassold; Giuseppe Amato 001 (Neurocomputing, 2024)

Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training. - Luca Ciampi; Gabriele Lagani; Giuseppe Amato 001; Fabrizio Falchi (CoRR, 2025)

Comparison of Different Deep Neural Network Models in the Cultural Heritage Domain. - Teodor Boyadzhiev; Gabriele Lagani; Luca Ciampi; Giuseppe Amato 001; Krassimira Ivanova (CoRR, 2025)

From Neurons to Computation: Biological Reservoir Computing for Pattern Recognition. - Ludovico Iannello; Luca Ciampi; Gabriele Lagani; Fabrizio Tonelli; Eleonora Crocco; Lucio Maria Calcagnile; Angelo Di Garbo; Federico Cremisi; Giuseppe Amato 001 (CoRR, 2025)