Principles and practice of information theory
Principles and practice of information theory
Elements of information theory
Elements of information theory
Differences in spiking patterns among cortical neurons
Neural Computation
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Can spike coordination be differentiated from rate covariation?
Neural Computation
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Information theoretic learning with adaptive kernels
Signal Processing
A characterization of the time-rescaled gamma process as a model for spike trains
Journal of Computational Neuroscience
Feature extraction from spike trains with Bayesian binning: `Latency is where the signal starts'
Journal of Computational Neuroscience
Kernel bandwidth optimization in spike rate estimation
Journal of Computational Neuroscience
Comparison of brain---computer interface decoding algorithms in open-loop and closed-loop control
Journal of Computational Neuroscience
Detection of hidden structures in nonstationary spike trains
Neural Computation
Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Automated modeling of stochastic reactions with large measurement time-gaps
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Improved similarity measures for small sets of spike trains
Neural Computation
Optimizing time histograms for non-poissonian spike trains
Neural Computation
Low bias histogram-based estimation of mutual information for feature selection
Pattern Recognition Letters
Random bin for analyzing neuron spike trains
Computational Intelligence and Neuroscience - Special issue on Computational Intelligence in Biomedical Science and Engineering
Interspike interval based filtering of directional selective retinal ganglion cells spike trains
Computational Intelligence and Neuroscience
Estimating neural firing rates: an empirical bayes approach
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Information transmission using non-poisson regular firing
Neural Computation
Key-dependent 3D model hashing for authentication using heat kernel signature
Digital Signal Processing
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The time histogram method is the most basic tool for capturing a time dependent rate of neuronal spikes. Generally in the neurophysiological literature, the bin size that critically determines the goodness of the fit of the time histogram to the underlying spike rate has been subjectively selected by individual researchers. Here, we propose a method for objectively selecting the bin size from the spike count statistics alone, so that the resulting bar or line graph time histogram best represents the unknown underlying spike rate. For a small number of spike sequences generated from a modestly fluctuating rate, the optimal bin size may diverge, indicating that any time histogram is likely to capture a spurious rate. Given a paucity of data, the method presented here can nevertheless suggest how many experimental trials should be added in order to obtain a meaningful time-dependent histogram with the required accuracy.