A. Paiva

Publications29
h-index11
Citations439
Highly Influential Citations30
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A comparison of binless spike train measures
TLDR
We present a systematic comparison of these 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. Expand
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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. Expand
  • 70
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A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning
TLDR
This paper provides a functional analysis perspective of information-theoretic learning (ITL) by defining bottom-up a reproducing kernel Hilbert space (RKHS) uniquely determined by the symmetric nonnegative definite kernel function known as the CIP. Expand
  • 56
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Sequential Monte Carlo Point-Process Estimation of Kinematics from Neural Spiking Activity for Brain-Machine Interfaces
TLDR
We have proposed a sequential Monte Carlo estimation methodology to reconstruct the kinematic states directly from the multichannel spike trains. Expand
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Nonlinear Component Analysis Based on Correntropy
TLDR
In this paper, we propose a new nonlinear principal component analysis based on a generalized correlation function which we call correntropy. Expand
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Kernel Methods on Spike Train Space for Neuroscience: A Tutorial
TLDR
We surveyed how positive definite functions can be used to quantify spike trains, at the rate or spike timing level, and implement important operations for neural decoding. Expand
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Self-organizing maps with dynamic learning for signal reconstruction
TLDR
We propose a dynamic learning rule for improved training of the SOM on signals with sparse events which allows for more representative prototype vectors to be found, and consequently better signal reconstruction. Expand
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A Monte Carlo Sequential Estimation for Point Process Optimum Filtering
TLDR
We propose a Monte Carlo sequential estimation methodology to estimate directly the posterior density. Expand
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Compression of Spike Data Using the Self-Organizing Map
TLDR
This paper presents a method for the compression of spike data. Expand
  • 10
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A fixed point update for kernel width adaptation in information theoretic criteria
TLDR
This paper presents a fixed point update for adaptation of the kernel width parameter in information theoretic criteria. Expand
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