Jérémie Lefebvre

The paper investigates how neuronal morphology affects the specificity of electric field stimulation at the single-cell level using computational models of mouse neurons.

This study identifies a mechanism by which h- and m-channel overexpression leads to seizure-like events through impaired processing of correlated inputs in inhibitory neurons, independent of changes in the excitatory-inhibitory balance.

The paper shows that within-cell-type electrophysiological heterogeneity is malleable and can be restored by blocking HCN currents in epilepsy models.

The paper examines how spatial myelination patterns in axons affect signaling reliability and injury susceptibility using computational models.

The paper develops a mathematical model of hippocampal circuits to identify cell-type specific mechanisms underlying theta-gamma coupled rhythms.

This study models how periodic brain stimulation can amplify post-stimulation oscillatory power through synaptic plasticity, showing that neuronal heterogeneity and plasticity are essential for lasting effects.

This study shows that excitability heterogeneity in neural networks enhances resilience by reducing dynamic volatility and making the system independent of control parameters.

This study investigates how tACS modulates synaptic plasticity in heterogeneous neural networks by exploiting differences in neuronal timescales.

The paper uses biophysical models to show that CCK+ basket cells have a greater role in theta-gamma coupling than PV+ basket cells in the hippocampus.

The paper shows that reduced neuronal heterogeneity in epileptogenic tissue decreases network resilience to synchrony changes, leading to seizure-like dynamics.

The paper introduces an Activity-Dependent Myelination (ADM) rule that optimizes neural synchrony by adjusting conduction velocity based on firing rates and axonal length variability.

The paper investigates how intrinsic neuronal properties and external inhibition influence theta rhythm frequencies in CA1 microcircuits using computational models.

This paper develops a connectome-based neural mass model to simulate state- and stimulation-dependent changes in rhythmic neural activity and connectivity, revealing how corticothalamic circuits modulate oscillatory patterns.

The paper modifies the Kuramoto model to include adaptive, phase-dependent delays in white matter networks, showing that these delays stabilize synchronization and enhance resilience to perturbations.

The paper analyzes how brain states and endogenous oscillations affect the response to periodic stimulation using a computational model of the thalamocortical system.