A. Paiva

Publications29
h-index11
Citations477
Highly Influential Citations35
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A comparison of binless spike train measures
TLDR
This paper presents a systematic comparison of several binless spike train measures in three simulated paradigms designed to address specific situations of interest in spike train analysis where the relevant feature may be in the form of firing rate, firing rate modulations, and/or synchrony.
A Reproducing Kernel Hilbert Space Framework for Spike Train Signal Processing
TLDR
This letter presents a general framework based on reproducing kernel Hilbert spaces (RKHS) to mathematically describe and manipulate spike trains to allow spike train signal processing from basic principles while incorporating their statistical description as point processes.
Sequential Monte Carlo Point-Process Estimation of Kinematics from Neural Spiking Activity for Brain-Machine Interfaces
TLDR
The performance of the sequential Monte Carlo estimation methodology augmented with this synthetic spike input provides improved reconstruction, which raises interesting questions and helps explain the overall modeling requirements better.
A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning
TLDR
All the statistical descriptors in the original information-theoretic learning formulation can be rewritten as algebraic computations on deterministic functional vectors in the ITL RKHS, instead of limiting the functional view to the estimators as is commonly done in kernel methods.
Nonlinear Component Analysis Based on Correntropy
TLDR
A new nonlinear principal component analysis based on a generalized correlation function which is called correntropy, which can efficiently compute the principal components in the feature space by projecting the transformed data onto those principal directions.
Kernel Methods on Spike Train Space for Neuroscience: A Tutorial
TLDR
This tutorial illustrates why kernel methods can change the way spike trains are analyzed and processed and provides a detailed overview of the current state of the art and future challenges.
Inner products for representation and learning in the spike train domain
Self-organizing maps with dynamic learning for signal reconstruction
A Monte Carlo Sequential Estimation for Point Process Optimum Filtering
TLDR
A Monte Carlo sequential estimation methodology to estimate directly the posterior density of the state given the observations is Gaussian distributed and reconstructs better the velocity when compared with point process adaptive filtering algorithm with the Gaussian assumption.
A fixed point update for kernel width adaptation in information theoretic criteria
  • A. Paiva, J. Príncipe
  • Computer Science
    IEEE International Workshop on Machine Learning…
  • 7 October 2010
TLDR
It is shown that the proposed fixed point update converges faster and is more stable when compared to a gradient update, and has no parameters, and can be simplified to achieve the same computational complexity as the stochastic gradient update.
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