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1,764
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
ORIGINAL ARTICLE A comparison of binless spike train measures
"... Abstract 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 analy ..."
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Abstract 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
An Empirical Study of Smoothing Techniques for Language Modeling
, 1998
"... We present an extensive empirical comparison of several smoothing techniques in the domain of language modeling, including those described by Jelinek and Mercer (1980), Katz (1987), and Church and Gale (1991). We investigate for the first time how factors such as training data size, corpus (e.g., Br ..."
Abstract
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Cited by 1224 (21 self)
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We present an extensive empirical comparison of several smoothing techniques in the domain of language modeling, including those described by Jelinek and Mercer (1980), Katz (1987), and Church and Gale (1991). We investigate for the first time how factors such as training data size, corpus (e
Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains
- Biophys. J
, 1967
"... AsTRAcT The statistical analysis oftwo simultaneously observed trainsofneuronal spikes is described, using as a conceptual framework the theory of stochastic point processes. The first statistical question that arises is whether the observed trains are independent; statistical techniques for testing ..."
Abstract
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Cited by 190 (2 self)
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AsTRAcT The statistical analysis oftwo simultaneously observed trainsofneuronal spikes is described, using as a conceptual framework the theory of stochastic point processes. The first statistical question that arises is whether the observed trains are independent; statistical techniques
A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms
, 2000
"... . Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thirty-two datasets in terms of classication accuracy, training time, and (in the case of trees) number of leaves. Classication accuracy is measured by mean error rate and mean rank of error rate. Both cr ..."
Abstract
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Cited by 234 (8 self)
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. Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thirty-two datasets in terms of classication accuracy, training time, and (in the case of trees) number of leaves. Classication accuracy is measured by mean error rate and mean rank of error rate. Both
The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis
- NEURAL COMPUTATION
, 2001
"... Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model’s validity prior to using it to make inferences about a particular neural system. Assessing goodness-of-fit is a challenging problem for point pro ..."
Abstract
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Cited by 131 (23 self)
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Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model’s validity prior to using it to make inferences about a particular neural system. Assessing goodness-of-fit is a challenging problem for point
Reproducibility and variability in neural spike trains
- Science
, 1997
"... To provide information about dynamic sensory stimuli, the pattern of action potentials in spiking neurons must be variable. To ensure reliability these variations must be related, reproducibly, to the stimulus. For H1, a motion-sensitive neuron in the fly’s visual system, constant-velocity motion pr ..."
Abstract
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Cited by 122 (11 self)
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To provide information about dynamic sensory stimuli, the pattern of action potentials in spiking neurons must be variable. To ensure reliability these variations must be related, reproducibly, to the stimulus. For H1, a motion-sensitive neuron in the fly’s visual system, constant-velocity motion
Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey
, 1996
"... edictably with stimulus parameters, it is widely held to be the primary variable relating neuronal response to visual experience (Adrian, 1928; Lettvin et al., 1959; Werner and Mountcastle, 1963; Barlow, 1972; Henry et al., 1973). Accordingly, many studies hold a stimulus parameter constant during a ..."
Abstract
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Cited by 163 (5 self)
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an experiment, measure large variations in firing frequency across different trials and high within-trial variation in inter-spike intervals, and conclude that the microstructure of spike trains is essentially random (Schiller et al., 1976; Heggelund and Albus, 1978; Tolhurst et al., 1981; Tolhurst et al., 1983
PROBEN1 - a set of neural network benchmark problems and benchmarking rules
, 1994
"... Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar problems. Proben1 contains 15 data sets from 12 different domains. All datasets ..."
Abstract
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Cited by 234 (0 self)
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a set of rules for how to conduct and how to document neural network benchmarking. The purpose of the problem and rule collection is to give researchers easy access to data for the evaluation of their algorithms and networks and to make direct comparison of the published results feasible
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