Dr. Rodica Curtu, Michigan Technological University, Department of Mathematical Sciences

Title: Discovering dynamical patterns of activity from single-trial intracranial neural recordings

Abstract: Discovering dynamical patterns from high fidelity time series is typically a challenging task. In this talk I will discuss a data-driven method for the analysis of neural recordings taken from the auditory cortex of human subjects who listened to sequences of repeated triplets of tones and reported their perception by pressing a button. Subjects reported spontaneous alternations between two auditory perceptual states: a galloping-like rhythm (1-stream) and a Morse-code like rhythm (2-streams). Our algorithm leverages time-delayed coordinates, diffusion maps, and dynamic mode decomposition, to identify neural features in large scale brain recordings that correlate with subject-reported perception. The method captures the dynamics of perception at multiple timescales and distinguishes attributes of neural encoding of the stimulus from those encoding the perceptual states. Our analysis reveals a set of latent variables that exhibit alternating dynamics along a low-dimensional manifold, like trajectories of attractor-based models.