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Optimization in reproducing kernel Hilbert spaces of spike trains
- IN COMPUTATIONAL NEUROSCIENCE
, 2010
"... This paper presents a framework based on reproducing kernel Hilbert spaces (RKHS) for optimization with spike trains. To establish the RKHS for optimization we start by introducing kernels for spike trains. It is shown that spike train kernels can be built from ideas of kernel methods, or from the i ..."
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Cited by 2 (2 self)
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This paper presents a framework based on reproducing kernel Hilbert spaces (RKHS) for optimization with spike trains. To establish the RKHS for optimization we start by introducing kernels for spike trains. It is shown that spike train kernels can be built from ideas of kernel methods, or from
Reliability and information transmission in spiking neurons, Trends in Neurosci
, 1992
"... 2), and co-workers identified many key features of this encoding. The rate of spike generation varies with the stimulus intensity (rate coding), the rate decreases in response to prolonged static stimulation (adaptation), and the rate can be affected by transformed versions of the stimulus, as in l ..."
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Cited by 50 (0 self)
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defines the probability per unit time of a spike occurring. White noise or Wiener kernel methods eliminate the need for repeating a particular stimulus waveform many times, allowing measurement of stimulus-rate relations for an entire stimulus ensemble rather than just one waveform from the ensemble
Spike train metrics Theoretical background
"... Quantifying similarity and dissimilarity of spike trains is an important requisite for understanding neural codes. Spike metrics constitute a class of approaches to this problem. In contrast to most signal-processing methods, spike metrics operate on time series of all-or-none events, and are, thus ..."
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Quantifying similarity and dissimilarity of spike trains is an important requisite for understanding neural codes. Spike metrics constitute a class of approaches to this problem. In contrast to most signal-processing methods, spike metrics operate on time series of all-or-none events, and are
An adaptive decoder from spike trains to micro-stimulationusing kernel least-mean-square (KLMS) algorithm
- In IEEE Machine learning for Signal Processing (MLSP
, 2011
"... This paper proposes a nonlinear adaptive decoder for so-matosensory micro-stimulation based on the kernel least mean square (KLMS) algorithm applied directly on the space of spike trains. Instead of using a binned representation of spike trains, we transform the vector of spike times into a function ..."
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Cited by 3 (2 self)
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This paper proposes a nonlinear adaptive decoder for so-matosensory micro-stimulation based on the kernel least mean square (KLMS) algorithm applied directly on the space of spike trains. Instead of using a binned representation of spike trains, we transform the vector of spike times into a
Inner products for representation and learning in the spike
, 2010
"... In many neurophysiological studies and brain-inspired computation paradigms, there is still a need for new spike train analysis and learning algorithms because current methods tend to be limited in terms of the tools they provide and are not easily extended. This chapter presents a general framework ..."
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Cited by 6 (4 self)
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framework to develop spike train machine learning methods by defining inner product operators for spike trains. They build on the mathematical theory of reproducing kernel Hilbert spaces (RKHS) and kernel methods, allowing a multitude of analysis and learning algorithms to be easily developed. The inner
Recording spikes from a large fraction of the ganglion cells in a retinal patch. Nat Neurosci.
, 2004
"... Simultaneous recording from most neurons in a neural circuit has not been accomplished anywhere in the vertebrate brain. The retina is promising for such a systematic study, because its modular organization implies that recording from a small patch of ganglion cells should sample its full functiona ..."
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Cited by 34 (3 self)
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, but rather in sorting the signals into spike trains from individual neurons. Despite considerable interest in algorithms designed to improve spike sorting, no general solution has emerged Here we report the development of a new method of multielectrode recording and spike sorting that uses a dense array
Journal of Neuroscience Methods 101 (2000) 93–106 Spike sorting based on discrete wavelet transform coefficients
, 2000
"... Using the novel mathematical technique known as wavelet analysis, a new method (WSC) is presented to sort spikes according to a decomposition of neural signals in the time–frequency space. The WSC method is implemented by a pyramidal algorithm that acts upon neural signals as a bank of quadrature mi ..."
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Using the novel mathematical technique known as wavelet analysis, a new method (WSC) is presented to sort spikes according to a decomposition of neural signals in the time–frequency space. The WSC method is implemented by a pyramidal algorithm that acts upon neural signals as a bank of quadrature
Unsupervised Spike Sorting Based on Discriminative Subspace Learning
"... Abstract — Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discrimi ..."
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Abstract — Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns
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