6 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…
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…
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…
Diagrammatic expansion for the mutual-information rate in the realm of limited statistics
Tobias Kühn, Gabriel Mahuas, Ulisse Ferrari
Neurons in sensory systems encode stimulus information into their stochastic spiking response. The mutual information has been extensively applied to these systems to quantify the…
Strong, but not weak, noise correlations are beneficial for population coding
Gabriel Mahuas, Thomas Buffet, Olivier Marre +2
Neural correlations play a critical role in sensory information coding. They are of two kinds: signal correlations, when neurons have overlapping sensitivities, and noise correlati…
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…