5 papers
A multi-scale information geometry reveals the structure of mutual information in neural populations
Simone Azeglio, Steeve Laquitaine, Ulisse Ferrari +1
Understanding how neural population responses represent sensory information is a central problem in systems neuroscience. One approach is to define a representational geometry on s…
Higher-Order Convolution Improves Neural Predictivity in the Retina
Simone Azeglio, Victor Calbiague Garcia, Guilhem Glaziou +3
We present a novel approach to neural response prediction that incorporates higher-order operations directly within convolutional neural networks (CNNs). Our model extends traditio…
Decomposing stimulus-specific sensory neural information via diffusion models
Steeve Laquitaine, Simone Azeglio, Carlo Paris +2
To understand sensory coding, we must ask not only how much information neurons encode, but also what that information is about. This requires decomposing mutual information into c…
Convolution goes higher-order: a biologically inspired mechanism empowers image classification
Simone Azeglio, Olivier Marre, Peter Neri +1
We propose a novel approach to image classification inspired by complex nonlinear biological visual processing, whereby classical convolutional neural networks (CNNs) are equipped…
Optimizing Urban Mobility Restrictions: a Multi-Agent System (MAS) for SARS-CoV-2
Simone Azeglio, Matteo Fordiani
Infectious epidemics can be simulated by employing dynamical processes as interactions on network structures. Here, we introduce techniques from the Multi-Agent System (MAS) domain…