Alessandro Torcini

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.

The paper investigates SVCA and PCA to determine reliable neural dimensionality, finding that reliable dimensionality saturates with increasing neuron count and decreases with higher correlation.

This paper proposes a mechanism for sustained balanced neural activity through short-term synaptic depression, showing it can maintain irregular activity without external inputs.

This study shows that slow cortical dynamics emerge in partially symmetric spiking neuron networks, leading to non-ergodic behavior with long-lived activity states.

This paper shows that Hebbian and anti-Hebbian inhibitory STDP rules enable stable memory encoding and modularity in spiking neural networks without control mechanisms.

The paper shows that discrete synaptic events can induce global oscillations in balanced neural networks, which are not captured by the diffusion approximation.

The paper demonstrates chaotic synchronization in a mean-field model of two heterogeneous populations of pulse-coupled oscillators with adaptive coupling strength.

The paper analyzes coexistence of asynchronous and clustered dynamics in inhibitory neural networks with noise.

The paper proposes a robust nonlinear balancing mechanism for spiking neural networks that maintains stability in the absence of strong external currents.

The paper shows that hippocampal gamma oscillations are diverse and behaviorally modulated, challenging the idea of rigid gamma sub-bands.

The paper shows inhibitory neurons are essential for forming and maintaining modular neural structures after learning through synaptic adaptation.

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.

This paper analyzes dynamical regimes in balanced neural networks using QIF neurons, showing that asynchronous activity or periodic oscillations can emerge depending on parameters, with mean field models capturing these behaviors.

This paper investigates the dynamics of excitatory-inhibitory neural networks, showing how balanced E-I populations can exhibit asynchronous and coherent behaviors, including oscillations and chaos, under different conditions.

The paper introduces a reduction methodology for heterogeneous spiking neural networks using pseudo-cumulants expansion to handle fluctuation-driven 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.

The paper analyzes asynchronous neural activity in a sparse network of leaky integrate-and-fire neurons, showing that strong synaptic coupling leads to bursting activity with long-tailed interspike intervals.

This paper shows that a single inhibitory neuron population can generate both slow and fast gamma oscillations through different mechanisms, and demonstrates how external stimuli can switch between these rhythms.

The paper shows that hippocampal network activity represents a combination of space and time with tunable degrees across days, using calcium imaging and network modeling in mice.

The paper develops a spiking network model to identify two types of driver cells in developing neuronal networks and explains their roles in synchronization dynamics.

The paper studies neural networks of leaky integrate-and-fire neurons, finding collective irregular dynamics as a distinct thermodynamic phase.

The paper shows that balanced networks of spiking neurons exhibit collective irregular dynamics in the thermodynamic limit under various conditions.

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.