21 citations · 36 across the 14 of their papers we have counts for
14 papers
Neuro-Inspired Visual Pattern Recognition via Biological Reservoir Computing
Luca Ciampi, Ludovico Iannello, Fabrizio Tonelli +4
In this paper, we present a neuro-inspired approach to reservoir computing (RC) in which a network of in vitro cultured cortical neurons serves as the physical reservoir. Rather th…
From Neural Activity to Computation: Biological Reservoirs for Pattern Recognition in Digit Classification
Ludovico Iannello, Luca Ciampi, Fabrizio Tonelli +5
In this paper, we present a biologically grounded approach to reservoir computing (RC), in which a network of cultured biological neurons serves as the reservoir substrate. This sy…
CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edge
Gabriele Lagani, Fabrizio Falchi, Claudio Gennaro +1
In this paper, we introduce a deep learning solution for video activity recognition that leverages an innovative combination of convolutional layers with a linear-complexity attent…
From Neurons to Computation: Biological Reservoir Computing for Pattern Recognition
Ludovico Iannello, Luca Ciampi, Gabriele Lagani +6
In this paper, we introduce a paradigm for reservoir computing (RC) that leverages a pool of cultured biological neurons as the reservoir substrate, creating a biological reservoir…
Comparison of Different Deep Neural Network Models in the Cultural Heritage Domain
Teodor Boyadzhiev, Gabriele Lagani, Luca Ciampi +2
The integration of computer vision and deep learning is an essential part of documenting and preserving cultural heritage, as well as improving visitor experiences. In recent years…
Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training
Luca Ciampi, Gabriele Lagani, Giuseppe Amato +1
Supervised deep learning achieves strong performance in biomedical image segmentation but relies on costly pixel-wise annotations, motivating semi-supervised approaches that exploi…
Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imaging
Luca Ciampi, Gabriele Lagani, Giuseppe Amato +1
We propose a novel bio-inspired semi-supervised learning approach for training downsampling-upsampling semantic segmentation architectures. The first stage does not use backpropaga…
Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey
Gabriele Lagani, Fabrizio Falchi, Claudio Gennaro +1
Recently emerged technologies based on Deep Learning (DL) achieved outstanding results on a variety of tasks in the field of Artificial Intelligence (AI). However, these encounter…
Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey
Gabriele Lagani, Fabrizio Falchi, Claudio Gennaro +1
For a long time, biology and neuroscience fields have been a great source of inspiration for computer scientists, towards the development of Artificial Intelligence (AI) technologi…
FastHebb: Scaling Hebbian Training of Deep Neural Networks to ImageNet Level
Gabriele Lagani, Claudio Gennaro, Hannes Fassold +1
Learning algorithms for Deep Neural Networks are typically based on supervised end-to-end Stochastic Gradient Descent (SGD) training with error backpropagation (backprop). Backprop…
Deep Features for CBIR with Scarce Data using Hebbian Learning
Gabriele Lagani, Davide Bacciu, Claudio Gallicchio +3
Features extracted from Deep Neural Networks (DNNs) have proven to be very effective in the context of Content Based Image Retrieval (CBIR). In recent work, biologically inspired \…
Hebbian Semi-Supervised Learning in a Sample Efficiency Setting
Gabriele Lagani, Fabrizio Falchi, Claudio Gennaro +1
We propose to address the issue of sample efficiency, in Deep Convolutional Neural Networks (DCNN), with a semi-supervised training strategy that combines Hebbian learning with gra…
Assessing Pattern Recognition Performance of Neuronal Cultures through Accurate Simulation
Gabriele Lagani, Raffaele Mazziotti, Fabrizio Falchi +5
Previous work has shown that it is possible to train neuronal cultures on Multi-Electrode Arrays (MEAs), to recognize very simple patterns. However, this work was mainly focused to…
Training Convolutional Neural Networks With Hebbian Principal Component Analysis
Gabriele Lagani, Giuseppe Amato, Fabrizio Falchi +1
Recent work has shown that biologically plausible Hebbian learning can be integrated with backpropagation learning (backprop), when training deep convolutional neural networks. In…