Andrea Galluzzi

This paper introduces a recurrent neural network model with heterogeneous self-interactions that generates scale-invariant memory and cognitive flexibility without requiring criticality.

The paper uses an RNN model to replicate motor inhibition dynamics in monkey premotor cortex, showing how internal network states influence behavior and movement suppression.

The paper investigates whether the brain operates near a synchronization critical point during cognitive tasks or only during resting wakefulness, finding critical dynamics in the awake state during resting periods.

The paper uses computational models to explain how cortical reactivity changes with adaptation, linking findings from human studies to theoretical frameworks.

The paper investigates network features of slow-wave activity under fading anesthesia in mice, finding that local excitability changes drive metastable patterns without altering connectivity.