Gaussian processes and beyond: From dynamical modeling to statistical signal processing

Contains fulltext : 205110.pdf (publisher's version ) (Open Access)Radboud University, 06 juni 2019Promotor : Medendorp, W.P. Co-promotor : Maris, E.G.G

Theta oscillations locked to intended actions rhythmically modulate perception

Contains fulltext : 175447.pdf (publisher's version ) (Open Access)Ongoing brain oscillations are known to influence perception, and to be reset by exogenous stimulations. Voluntary action is also accompanied by prominent rhythmic activity, and recent behavioral evidence suggests that this might be coupled with perception. Here, we reveal the neurophysiological underpinnings of this sensorimotor coupling in humans. We link the trial-by-trial dynamics of EEG oscillatory activity during movement preparation to the corresponding dynamics in perception, for two unrelated visual and motor tasks. The phase of theta oscillations (~4 Hz) predicts perceptual performance, even >1 s before movement. Moreover, theta oscillations are phase-locked to the onset of the movement. Remarkably, the alignment of theta phase and its perceptual relevance unfold with similar non-monotonic profiles, suggesting their relatedness. The present work shows that perception and movement initiation are automatically synchronized since the early stages of motor planning through neuronal oscillatory activity in the theta range.18 p

GP CaKe: Effective brain connectivity with causal kernels

Contains fulltext : 179527.pdf (publisher's version ) (Open Access)31st Conference on Neural Information Processing Systems (NIPS 2017) (Long Beach, CA, USA, December 4 - 9, 2017

Complex-valued Gaussian process regression for time series analysis

The construction of synthetic complex-valued signals from real-valued observations is an important part of many time series analysis techniques. The most widely used approach is based on the Hilbert transform, which maps the real-valued signal into its quadrature component. In this paper, we define a probabilistic generalization of this approach. We model the observable real-valued signal as the real part of a latent complex-valued Gaussian process. In order to obtain the appropriate statistical relationship between its real and imaginary parts, we define two new classes of complex-valued covariance functions. Through an analysis of stochastic oscillations, we show that the resulting Gaussian process complex-valued signal provides a better estimate of the instantaneous amplitude and frequency than the established approaches. Furthermore, the complex-valued Gaussian process regression allows to incorporate prior information about the structure in signal and noise and thereby to tailor the analysis to the features of the signal. As a example, we analyze the non-stationary dynamics of brain oscillations in the alpha band, as measured using magneto-encephalography

Rinvenimento di Globodera tabacum su tabacco in Campania e sua distribuzione geografica

Atti VI Congresso Nazionale Nematologi

Algorithmic composition of polyphonic music with the WaveCRF

Contains fulltext : 179506.pdf (publisher's version ) (Open Access)Here, we propose a new approach for modeling conditional probability distributions of polyphonic music by combining WaveNET and CRF-RNN variants, and show that this approach beats LSTM and WaveNET baselines that do not take into account the statistical dependencies between simultaneous notes.NIPS 2017: 31st Annual Conference on Neural Information Processing Systems (Long Beach, California, December 4-9, 2017