Giovanni Diana

This paper introduces a Bayesian method using particle Gibbs sampling to infer high-frequency neuronal spikes and quantify their uncertainty, showing improved performance on benchmark datasets.

The paper shows that synaptic dynamics shape sparse neural activity patterns in granule cells, tuning them to match learning demands.

The paper maps spatial subdomains in the zebrafish optic tectum that encode different visual features using information theory.

The paper develops biophysically-inspired models to improve spike inference from calcium imaging by accounting for kinetic features of fluorescent indicators.

The paper shows that tectal neurons in larval zebrafish exhibit sharpened spatial tuning to prey-like stimuli when presented in natural scenes, confined to a specific visual space region associated with hunting behavior.

The paper shows that EI imbalance during seizures disrupts whole-brain critical dynamics by reorganizing neural networks, leading to chaotic and less stable brain states.

This study shows how C. elegans maintains food-responsiveness across temperature changes through a multicellular gene network involving TGF-β and serotonin signaling.

This study shows cadherins regulate neuronal positioning and function in zebrafish ocular motor nuclei by affecting subnuclear topography and motor reflexes.

The paper quantifies progenitor cell outputs in the developing mouse neocortex, showing stochastic lineage generation of pyramidal cells leading to diverse laminar configurations.

The paper introduces a Bayesian generative model to analyze neuronal ensemble activity, applied to zebrafish and mouse data to study ensemble structure, dynamics, and behavior-related activity.