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Peristimulus time histograms are a widespread form of visualizing neuronal responses. Kernel convolution methods transform these histograms into a smooth, continuous probability density function. This provides an improved estimate of a neuron’s actual response envelope. In a recent publication we developed a classifier, called the h-coefficient, to determine whether time-locked fluctuations in the firing rate of a neuron should be classified as a response or as random noise. Unlike previous approaches, the h-coefficient takes advantage of the more precise response envelope estimation provided by the kernel convolution method. The h-coefficient quantizes the smoothed response envelope and calculates the probability of a response of a given shape to occur by chance. Please refer to the original publication for further information.
Cite As
Michael (2026). h-coefficient (https://uk.mathworks.com/matlabcentral/fileexchange/48293-h-coefficient), MATLAB Central File Exchange. Retrieved .
Acknowledgements
Inspired by: Shade area between two curves, Kernel Density Estimator
General Information
- Version 1.2 (62.4 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
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| 1.2 | Minor bug fixes during launch phase... |
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| 1.1.0.0 | Minor bug fixes during launch phase... |
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