Fabiano Baroni

This paper introduces ExSEnt, a novel entropy-based measure for dementia detection using EEG, showing it achieves comparable classification performance to deep learning methods with a simpler logistic regression model.

The paper evaluates 122 measures for analyzing synchrony, oscillations, and phase relationships in neuronal activity across synthetic and biological spike trains, highlighting method-specific biases and guiding their use.

The paper examines neuromuscular dynamics during a finger movement task, finding that fast responders show distinct EEG and EMG patterns linked to motor inhibition and activation.

The paper shows how dynamic synapses and intrinsic subthreshold oscillations together enable neurons to recognize specific temporal patterns in spike trains, with robustness to noise and synaptic strength variations.

The paper investigates dual timescale processing in human auditory cortex using EEG, modeling, and decoding, finding bimodal spectral and informational patterns consistent with a dual subnetwork model.