Maxim Bazhenov

This study shows that slow brain fluctuations in resting states arise from ion concentration dynamics modeled in a computational brain network.

The paper identifies coordinated autonomic and central events during NREM sleep that predict declarative memory consolidation.

The paper examines how increasing the integration time step affects a hippocampal CA3 network model and proposes parameter adjustments to mitigate the impact.

The paper develops a computational model of brainstem circuits to study how noradrenergic and serotonergic drives affect hypoglossal motoneurons during REM sleep, aligning with in vivo data.

This study uses a computational model to show that learning-induced synaptic changes increase the reactivation of recently learned trajectories during sleep SWR events.

This study investigates how varying concentrations of 4-aminopyridine (4AP) affect hippocampal epileptiform activity in mouse brain slices, showing dose-dependent changes in frequency and spatio-temporal patterns.

The paper proposes a biophysical model of homeostatic plasticity that explains how astrocytes detect and respond to prolonged synaptic inactivity through metabotropic glutamate receptors and TNF signaling.

This paper develops a theoretical model of hippocampal spiking during sharp-wave ripples, showing how network connectivity and inhibition shape reactivation patterns in CA3 and CA1.

This study investigates the role of KCC2-dependent potassium efflux in 4-aminopyridine-induced epileptiform synchronization through experiments and a biophysically realistic network model.