Cristiano Capone

This paper trains a generative model on neural data to reveal violations of the Independent Race Model's assumptions in a stop task.

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 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.

The paper develops a mean-field formalism to predict the collective dynamics of large networks of complex spiking neurons, linking population models to electrophysiology.

The paper identifies two types of slow waves in the cerebral cortex, showing they differ in neural dynamics and sensitivity to stimuli, explaining differences in memory consolidation under sleep and anesthesia.

The paper develops a mean-field model of spiking neuron networks with adaptation to accurately capture both spontaneous and evoked activity in large-scale neural models.

The paper modifies a generalized linear model to better capture non-stationary and non-linear spiking dynamics in neural networks using in-vitro data.