Alain Destexhe A

The paper builds a whole-brain model of auditory discrimination in macaques using The Virtual Brain framework and AdEx neuronal models.

The paper compares self-sustained and driven neural networks, finding differences in dynamical properties and responsiveness, with implications for identifying network types from experimental data.

The paper investigates how consciousness states affect brain structure-function organisation using anaesthesia and deep brain stimulation in non-human primates.

This paper develops a mean-field neural model of brain states that reproduces empirical dynamics across scales and brain states, using the human connectome and the Virtual Brain simulator.

This paper develops computational models to study seizure propagation in neural networks, showing that inhibitory connectivity and network structure determine seizure spread and identifying a time window for intervention.

The paper shows that awake cortex forms stimulus-specific neuronal assemblies for sensory inputs, distinct from ongoing activity, while anesthesia blurs this distinction.

The paper challenges the attractor model of cortical activity by showing core neurons have weaker connections and funnel information flows instead of forming strong recurrent networks.

The paper analyzes REM sleep activity in mice using experimental data and a computational model, finding SWA is prominent in somatosensory areas and linked to reduced cortical responsiveness.

This paper measures impedance of neural tissue across different preparations and shows frequency-dependent behavior that cannot be explained by resistive models, attributing it to ionic diffusion in Debye layers.

The paper reviews experimental findings and computational models of fast dendritic spikes in rats, highlighting their role in enhancing neuronal computational capacity and network complexity.

This study uses computational models to examine how gamma oscillations and asynchronous states affect neural network responsiveness to external stimuli, finding that asynchronous states provide higher responsiveness.

This paper introduces a model showing astrocytic calcium dynamics link neuronal activity to the BOLD signal, explaining key features of neurovascular coupling.

The paper develops a brain-wide model using biologically realistic population dynamics to simulate slow-wave synchrony and asynchronous states, showing their distinct responses to stimulation.

This paper introduces the Conductance-based Adaptive Exponential integrate-and-fire (CAdEx) model to address limitations of the AdEx model in simulating neuronal behavior.

The paper introduces a kernel-based method to calculate local field potentials (LFPs) from networks of spiking neurons using unitary LFPs and experimental data.

This study integrates experimental and computational models of motor control and recovery after stroke to improve experimental design through continuous data integration and simulation.

The paper models the contribution of excitatory and inhibitory synapses to local field potentials in the hippocampus, showing inhibitory uLFPs dominate and mask excitatory signals.

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 investigates how suppressive waves in V1 shape the representation of long-range apparent motion using voltage-sensitive dye imaging in monkeys.

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 shows that neurons with active dendrites have reduced firing rates in response to correlated synaptic input, contrasting with point neurons or passive dendritic models.

This study uses Maximum Entropy models to analyze neural activity in human and monkey cortex, revealing that wakefulness involves pairwise interactions while sleep involves population-wide interactions.

The paper investigates how the axon initial segment contributes to extracellular action potential signatures, showing dipole formation and changes in waveform based on initiation site.

The paper develops a mean-field model of neural networks to simulate and predict voltage-sensitive dye imaging signals in the visual cortex.

The paper shows that refractoriness explains variable spike burst responses in somatosensory cortex by combining synaptic inputs with intrinsic neural properties.

The paper develops a theoretical framework to analyze how firing responses in layer V pyramidal neurons vary with presynaptic activity, revealing heterogeneous coupling mechanisms.

The paper investigates the contribution of spiking neurons to local field potentials (LFP) in human and monkey cortex, finding that LFP primarily reflects inhibitory neuron activity.