Ruben Coen-Cagli

This paper presents a multimodal framework combining EEG, eye-tracking, and behavioral responses to study natural scene segmentation in ASD and NT individuals, revealing cohort differences in perception and attention.

This study reveals that iterative Bayesian inference explains the spatiotemporal dynamics of natural scene segmentation by integrating visual inputs with spatial priors.

The paper introduces a mathematical framework to analyze how neural population variability affects sensory information transmission between brain areas using communication subspaces.

This study shows that adult neurogenesis in mice enhances spatial information encoding in the hippocampus during novel context exposure.

This study examines the structure of trial-by-trial neural co-variance in dropout neural networks and finds it similar to biological neural variability, suggesting potential implications for network performance and modeling.

The paper introduces a statistical model of divisive normalization's effects on noise correlations in neural populations and validates it using mouse visual cortex data.

This paper introduces response models based on mixture models and exponential families to quantify neural coding properties in macaque primary visual cortex, capturing noise correlations and enabling Bayesian decoding.

The paper investigates the role of higher-order texture statistics in natural image segmentation and finds they provide only modest improvements over spectral statistics.

The paper shows that variability in macaque V1 neurons reflects probabilistic inference tuned to natural image statistics, explaining variance-mean relationships and spatial context modulation.

The paper investigates how contextual modulation interacts with segmentation in human peripheral vision using naturalistic textures, showing that segmentation cues affect contextual modulation not captured by summary-statistics models.

The paper quantifies how divisive normalization affects neural response variability in macaque primary visual cortex using a stochastic normalization model.