FARFAN Fernando Daniel
congresos y reuniones científicas
Nonlinear dynamic analysis of afferent discharges from vibrissal nerve based on noise-assisted multivariate empirical mode decomposition
ANA LÍA ALBARRACÍN; ALVARO GABRIEL PIZÁ; JORGE HUMBERTO SOLETTA; FACUNDO LUCIANNA; JUAN CARLOS SORIA; FERNANDO DANIEL FARFÁN; CARMELO JOSÉ FELICE
Congreso; 2do FALAN Congress 2016; 2016
Federation of Latin American and Caribbean Neuroscience Societies
Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world 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. Furthermore, nonlinear features from afferent discharges reveals precise correlations with the applied stimulus. The methods proposed in this study give rise to new analysis approaches, while also adequately conform to the nonlinear characteristics of neural responses.