Topic: dynamic mode decomposition

Discovering dynamical patterns from high fidelity timeseries is typically a challenging task. In this talk, the timeseries data consist 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 (1-stream and 2-streams). We discuss a data-driven method, which leverages time-delayed coordinates, diffusion maps, and dynamic mode decomposition, to identify neural features that correlated with subjec

From the periodic spiking of single neurons to brain rhythms, oscillations are a ubiquitous neural phenomenon. Phase reduction is a powerful way of analysing deterministic systems exhibiting such oscillatory behavior: the N-dimensional state of the system is encoded into a one dimensional phase variable via projection onto the closed limit cycle attractor. Such a reduction gives rise to one-dimensional models that have been used to model a variety of single cell (e.g. theta-neuron) and network (e.g. Kuramoto oscillator networks) dynamics. However, real-world oscillations are often noisy and b