Simona Olmi

This paper studies slow relaxation oscillations in neural mass models with spike frequency adaptation, identifying chaotic behaviors and their relation to Up and Down states.

This paper improves a genetic score for predicting COVID-19 severity by incorporating organ-specific weights derived from Boolean quantum features and genetic algorithms.

This study examines how spike-frequency adaptation affects macroscopic neural dynamics in excitatory and inhibitory networks, revealing new collective dynamical regimes and cross-frequency coupling patterns.

The paper uses a neural mass model combined with patient-specific brain networks to simulate seizure propagation and test clinical hypotheses about epileptic seizure dynamics.

This paper develops a neural mass model that exactly reproduces the dynamics of spiking neural networks with synaptic plasticity, showing how memory operations emerge through oscillatory activity and synaptic mechanisms.

This paper studies theta-nested gamma oscillations in neural mass models, showing they can emerge from two network configurations with distinct locking behaviors and frequency dynamics.

This study investigates cross-frequency coupling in inhibitory neural mass models, showing how different synaptic parameters can generate theta-gamma rhythms and their coupling mechanisms.

This paper develops a seizure control strategy using patient-specific brain network models to predict and modulate seizure propagation patterns in epilepsy patients.

The paper analyzes the firing time statistics of neurons under inhibitory noise, deriving exact results for firing rate, coefficient of variation, and spike train spectrum.

The paper investigates how inhibition in sparse neural networks can both suppress and promote neural activity, revealing a transition between different firing regimes.