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23
A comparison of binless spike train measures
, 2009
"... Several binless spike train measures which avoid the limitations of binning have been recently been proposed in the literature. This paper presents a systematic comparison of these measures in three simulated paradigms designed to address specific situations of interest in spike train analysis where ..."
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Cited by 12 (1 self)
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Several binless spike train measures which avoid the limitations of binning have been recently been proposed in the literature. This paper presents 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. The measures are first disseminated and extended for ease of comparison. It is also discussed how the measures can be used to measure dissimilarity in spike trains’ firing rate despite their explicit formulation for synchrony.
Quantification of intertrial nonstationarity in spike trains from periodically stimulated neural cultures
 In IEEE International conference on acoustics, speech, and signal processing (ICASSP
, 2010
"... In neuroscience, nonstationarity detection of spike trains is useful for ensuring stability of experimental condition, and detecting plasticity. A novel method for estimating point process divergence and its application for nonstationarity detection in spike trains is proposed. The method for me ..."
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Cited by 9 (7 self)
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In neuroscience, nonstationarity detection of spike trains is useful for ensuring stability of experimental condition, and detecting plasticity. A novel method for estimating point process divergence and its application for nonstationarity detection in spike trains is proposed. The method for measuring divergence is based on decomposition of finite point process and Hilbertian metrics. The method is demonstrated by detecting shortterm and longterm plasticity in neural culture probed with periodic stimulations. Index Terms — nonstationarity detection, point process divergence, finite point process 1.
Inner products for representation and learning in the spike
, 2010
"... In many neurophysiological studies and braininspired 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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In many neurophysiological studies and braininspired 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 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 products utilize functional representations of spike trains, which we motivate from two perspectives: as a biologicalmodeling problem, and as a statistical description. The biologicalmodeling approach highlights the potential biological mechanisms taking place at the neuron level and that are quantified by the inner product. On the other hand, by interpreting the representation from a statistical perspective, one relates to other work in the literature. Moreover, the statistical description characterizes which information can be detected by the spike train inner product. The applications of the given inner products for development of machine learning methods are demonstrated in two
On the efficient calculation of van Rossum distances
 Network
, 2012
"... Abstract The van Rossum metric measures the distance between two spike trains. Measuring a single van Rossum distance between one pair of spike trains is not a computationally expensive task, however, many applications require a matrix of distances between all the spike trains in a set or the calcu ..."
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Cited by 3 (1 self)
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Abstract The van Rossum metric measures the distance between two spike trains. Measuring a single van Rossum distance between one pair of spike trains is not a computationally expensive task, however, many applications require a matrix of distances between all the spike trains in a set or the calculation of a multineuron distance between two populations of spike trains. Moreover, often these calculations need to be repeated for many different parameter values. An algorithm is presented here to render these calculation less computationally expensive, making the complexity linear in the number of spikes rather than quadratic.
An adaptive decoder from spike trains to microstimulationusing kernel leastmeansquare (KLMS) algorithm
 In IEEE Machine learning for Signal Processing (MLSP
, 2011
"... This paper proposes a nonlinear adaptive decoder for somatosensory microstimulation 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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This paper proposes a nonlinear adaptive decoder for somatosensory microstimulation 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 in reproducing kernel Hilbert space (RKHS), where the inner product of two spike time vectors is defined by a nonlinear cross intensity kernel. This representation encapsulates the statistical description of the point process that generates the spike trains, and bypasses the curse of dimensionalityresolution of the binned spike representations. We compare our method with two other methods based on binned data: GLM and KLMS, in reconstructing biphasic microstimulation. The results indicate that the KLMS based on RKHS for spike train is able to detect the timing, the shape and the amplitude of the biphasic stimulation with the best accuracy. Index Terms — Adaptive Neural decoder, KLMS, spike train, microstimulation
Which measure should we use for unsupervised spike train learning?
"... In certain experimental paradigms, the dynamics of a neural system may not be fully determined by external stimuli because the neural activity depends on internal states from a wide range of possible causes. For example, bistable dynamics of a single neuron has been observed in vitro via frozen nois ..."
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Cited by 1 (1 self)
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In certain experimental paradigms, the dynamics of a neural system may not be fully determined by external stimuli because the neural activity depends on internal states from a wide range of possible causes. For example, bistable dynamics of a single neuron has been observed in vitro via frozen noise injection [1], local field potentials and EEG phase often correlates with response strength, and topdown control such as attention are known to affect responses. In other words, in these cases, it is nearly impossible to control or observe all the internal variables. Still, we would like to infer these internal states by analyzing the observation variability. The solution we propose is to use unsupervised learning methods, such as PCA and clustering [2, 3], to discover the internal states. Spike train measures Consider two spike trains si, sj ∈ S(T) defined in the interval T = [0, T]. We compare three inner product measures: • The memoryless crossintensity (mCI) inner product is defined as I(si, sj) = T λsi
A TensorProductKernel Framework for Multiscale Neural Activity Decoding and Control
, 2014
"... Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain’s motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field ..."
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Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain’s motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensorproductkernelbased framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to
Perievent CrossCorrelation over Time for Analysis of Interactions in Neuronal Firing
"... Abstract — Several methods have been described in the literature to verify the presence of couplings between neurons in the brain. In this paper we introduce the perievent crosscorrelation over time (PECCOT) to describe the interaction among the two neurons as a function of the event onset. Instead ..."
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Abstract — Several methods have been described in the literature to verify the presence of couplings between neurons in the brain. In this paper we introduce the perievent crosscorrelation over time (PECCOT) to describe the interaction among the two neurons as a function of the event onset. Instead of averaging over time, the PECCOT averages the crosscorrelation over instances of the event. As a consequence, the PECCOT is able to characterize with high temporal resolution the interactions over time among neurons. To illustrate the method, the PECCOT is applied to a simulated dataset and for analysis of synchrony in recordings of a rat performing a go/no go behavioral lever press task. We verify the presence of synchrony before the lever press time and its suppression afterwards. I.