Continuous functions determined by spike trains of a neuron subject to stimulation
Biological Cybernetics
Disambiguating different covariation types
Neural Computation
Correlations without synchrony
Neural Computation
Information-geometric measure for neural spikes
Neural Computation
Mean instantaneous firing frequency is always higher than the firing rate
Neural Computation
Statistical Signs of Common Inhibitory Feedback with Delay
Neural Computation
Can spike coordination be differentiated from rate covariation?
Neural Computation
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We present an estimate for the instantaneous discharge probability of a neurone, based on single-trial spike-train analysis. By detecting points where the neurone abruptly changes its firing rate and treating them specially, the method is able to achieve smooth estimates yet avoid the blurring of significant changes. This estimate of instantaneous discharge probability is then applied to the method of unitary event analysis. We show that the unitary event analysis as originally conceived is highly sensitive to firing-rate nonstationarities and covariations, but that it can be considerably improved if calculations of statistical significance use an instantaneous discharge probability instead of a firing-rate estimate based on averaging across multiple trials.