Marco Celotto

The paper introduces MICs to analyze how modulators reshape interactions between neural populations using a bilinear perturbation framework, showing its effectiveness in simulations and real neural data.

This study identifies CDKL5-mediated phosphorylation of nELAVL as critical for regulating RNA metabolism and condensate communication, essential for visual cortex maturation.

The paper shows that brain activity in the parietal cortex encodes evidence consistently with prior choices, leading to confirmation bias in decision-making.

MINT is a toolbox for analyzing multivariate neural information coding and transmission using information theory and dimensionality reduction, applied to simulated and real neural data.

The paper shows that cortical astrocytes mediate learned behavior by extending norepinephrine signals through calcium dynamics and ATP release, influencing neuronal encoding of task variables.

The paper develops bias correction methods for Partial Information Decomposition (PID) in neural data, improving estimates of synergy and redundancy in mouse brain regions.

This study shows that sensory information increases in primary somatosensory cortex during learning, while choice information emerges later and relies on population coding.

The paper measures redundant and synergistic functional connectivity in mouse auditory cortex during a perceptual task, finding that connected neuron pairs show more redundancy and less synergy than unconnected pairs.

This paper introduces a new information-theoretic measure, FIT, to quantify feature-specific information transfer between brain regions using simulated and empirical neural data.

NIT is an open-source toolbox for information-theoretic analysis of neural data, including calcium imaging, offering tools for encoding quantification, information transmission, and partial information decompositions.