Stéphane Deny

The paper introduces a texture backward masking method to identify critical recurrent processes in human vision by targeting different stages of the visual system.

This study evaluates how different noises affect a DNN-based gesture recognition system using HD-sEMG, finding that feature type and noise type influence performance degradation.

The paper presents a method to predict population-level neural activity from sequential single-cell recordings using copula distributions and maximum entropy modeling, showing increased synchronous activity with larger populations.

The paper demonstrates causal coupling between neural activity, metabolism, and behavior in the Drosophila brain using simultaneous measurements of activity and metabolic flux across behavioral states.

Imposing an optic-nerve-like bottleneck in a deep convolutional neural network trained on image recognition causes antagonistic center-surround retinal receptive fields and orientation-tuned primary visual cortex representations to emerge simultaneously.

This study demonstrates that optogenetically activated ganglion cells in mouse and macaque retinas have high spatial resolution, predicting acuity above legal blindness limits.

Optimizing linear and rectifying nonlinear retinal encoding models on natural movies under a fixed firing-rate budget yields ON and OFF midget- and parasol-like ganglion cell types matching primate retina organization.

This paper introduces a method to separate intrinsic neuron interactions from extrinsic correlations in sensory neural networks using mean-field corrections, validated on retinal recordings.

The paper introduces a simple model that corrects Poisson statistics to better predict low variability in neural spike trains, showing improved accuracy in retinal recordings.

The paper shows that a single type of retinal ganglion cell encodes two different visual features depending on stimulus context.