Technical Program

Paper Detail

Paper: PS-2A.22
Session: Poster Session 2A
Location: Symphony/Overture
Session Time: Friday, September 7, 17:15 - 19:15
Presentation Time:Friday, September 7, 17:15 - 19:15
Presentation: Poster
Publication: 2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania
Paper Title: Spectral Power Variation Separates Oscillatory from Non-Oscillatory Stochastic Neural Dynamics
Manuscript:  Click here to view manuscript
Authors: Richard Gao, Lauren Liao, Bradley Voytek, University of California, San Diego, United States
Abstract: Oscillatory brain activity observed in neural field potentials has been widely used to index behavior, cognition, and disease. There is emerging evidence, however, that oscillations can exist in different modes, such as sustained versus bursting, that have different physiological origins and different behavioral relevance. Additionally, there exist other, non-oscillatory components in the field potential that can obscure or be mistaken for oscillations, especially in the mean power spectral density (PSD). One such component is the aperiodic signal that gives rise to the 1/f power law background in the field potential PSD, which has been proposed to reflect synaptic potentials induced by Poisson population spiking. It remains an ongoing challenge to consistently define, operationalize, and isolate oscillatory and non-oscillatory neural dynamics. In this work, we begin with a model of the field potential as a superposition of the aperiodic (Poisson) component and oscillatory components. We use two measures – spectral coefficient of variation (SCV) and deviation from noise power distribution – that are able to separate these components in simulation. Finally, we demonstrate the existence and separation of these components in a range of experimental data, focusing on human electrocorticography during movement in this paper.