Nonlinear dynamic analysis of afferent discharges from vibrissal nerve based on noise-assisted multivariate empirical mode decomposition

ALBARRACÍN, ANA LÍA; PIZÁ, ALVARO GABRIEL; SOLETTA, JORGE HUMBERTO; LUCIANNA, FACUNDO ADRIÁN; SORIA, JUAN CARLOS; FARFÁN, FERNANDO DANIEL; FELICE, CARMELO JOSÉ

Buenos Aires

Congreso; 2nd FALAN Congress; 2016

La Federación de Asociaciones Latinoamericanas y del Caribe de Neurociencias (FALAN)

Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to realworld stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches. In this study, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing the dynamic of peripheral afferent discharges in a multi-dimensional space with instantaneous frequency (IF) resolution. The proposed approach was able to extract information of neurophysiological data of deep vibrissal nerve recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis. Texture discrimination analysis performance was increased when NA-MEMD plus Hilbert transform was implemented, compared to linear techniques. The methods proposed in this study give rise to new analysis approaches, while also adequately conform to the nonlinear characteristics of neural responses.