Maurizio Mattia

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

This study shows that the premotor cortex encodes a mental line for transitive inference, with neural activity reflecting symbolic distances and learning through geometric transformations.

This paper introduces NEBULA, a framework that models whole-brain neural activity and behavior in C. elegans, identifying shared dynamics for prediction and intervention.

This paper uses reservoir computing with noisy linear recurrent neural networks to create digital twins of brain activity from resting-state fMRI data, enabling prediction and decomposition of BOLD signals into low-dimensional latent states.

This study investigates how direct current stimulation modulates slow and fast cortical oscillations in vitro, finding precise effects on slow oscillations and variable effects on high frequencies.

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.

This study uses computational models to investigate how local and global network properties shape cortical slow wave activity and propagation.

This study examines SWA dynamics during mouse awakening from anesthesia, showing state-dependent changes in spectral power and propagation patterns across brain regions.

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 proposes a geometric model for serial learning that explains transitive inference behavior in animals and motor decision-making in recurrent networks.

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

This study investigates neuronal dynamics in non-human primates during motor plan cancellation, finding that inhibition and position holding involve confined neuronal activity in a subspace, while movement execution requires transitioning to an orthogonal subspace.

This study investigates the laminar organization of slow oscillations in anesthetized rat cortex, finding that Up states originate in layer 6 and exhibit hysteresis influenced by columnar circuitry.

The paper shows that cortical dynamics during awakening from anesthesia involve a transition from slow oscillations to infra-slow alternations, driven by attractor competition in neuronal networks.

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.

The paper shows that multistable perception, like binocular rivalry, is reproduced by a hierarchical dynamics model that integrates evidence and evaluates alternatives in a volatile environment.

The paper introduces a state-dependent mean-field formalism to model activity states in conductance-based neuronal networks, showing it can predict firing rates and population dynamics in different regimes.