Dr. Curtu's research combines methods from nonlinear dynamical systems with computer simulations and statistical methods to study the dynamics of neuronal networks induced by the properties of local network components, by the properties of the coupling, by the network topology, and by the stimulus structure. The lab employs a diverse range of techniques to identify and characterize relevant spatiotemporal patterns in brain data - human psychophysics, signal processing, classifiers, dimensionality reduction and manifold learning. Recent projects include modeling of human auditory perceptual switching informed by large-scale ECoG recordings, and dynamic models for auditory category learning.

Recent publications