Aurélie Pala

The paper uses a hidden Markov model to relate latent dynamics of neural spiking activity in the somatosensory cortex to classical brain state metrics like local field potential fluctuations and behavior.

This study investigates how cortical layer 6 feedback neurons modulate thalamic activity in awake mice, showing they can suppress or enhance thalamic spiking based on firing rate and synchrony.

This paper develops real-time cortical state estimation algorithms using Gaussian Mixture Models and Hidden semi-Markov Models, showing they can robustly infer cortical states from electrophysiological data.

The paper examines membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during different behavioral states, revealing distinct activity patterns based on neuron type and cortical depth.

The paper investigates ipsilateral stimulus encoding in primary and secondary somatosensory cortex of awake mice, finding that S2 encodes ipsilateral stimuli more accurately than S1.

The paper develops a statistical framework to infer monosynaptic connectivity in vivo using extracellular recordings from rodent thalamocortical pathways.

The paper improves sensory input detection in awake mice by using a cortical state classifier to account for trial-to-trial variability in neural responses.