Name
Abstract | ||
|---|---|---|
We propose using the statistical measurement of the sample skewness of the distribution of mean firing rates of a tuning curve to quantify sharpness of tuning. For some features, like binocular disparity, tuning curves are best described by relatively complex and sometimes diverse functions, making it difficult to quantify sharpness with a single function and parameter. Skewness provides a robust nonparametric measure of tuning curve sharpness that is invariant with respect to the mean and variance of the tuning curve and is straightforward to apply to a wide range of tuning, including simple orientation tuning curves and complex object tuning curves that often cannot even be described parametrically. Because skewness does not depend on a specific model or function of tuning, it is especially appealing to cases of sharpening where recurrent interactions among neurons produce sharper tuning curves that deviate in a complex manner from the feedforward function of tuning. Since tuning curves for all neurons are not typically well described by a single parametric function, this model independence additionally allows skewness to be applied to all recorded neurons, maximizing the statistical power of a set of data. We also compare skewness with other nonparametric measures of tuning curve sharpness and selectivity. Compared to these other nonparametric measures tested, skewness is best used for capturing the sharpness of multimodal tuning curves defined by narrow peaks (maximum) and broad valleys (minima). Finally, we provide a more formal definition of sharpness using a shape-based information gain measure and derive and show that skewness is correlated with this definition. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1162/NECO_a_00582 | Neural Computation |
Keywords | Field | DocType |
biomedical research,bioinformatics | Sharpening,Parametric equation,Skewness,Nonparametric statistics,Maxima and minima,Invariant (mathematics),Artificial intelligence,Neuronal tuning,Statistical power,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
26 | 5 | 0899-7667 |
Citations | PageRank | References |
1 | 0.36 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jason M. Samonds | 1 | 1 | 1.04 |
| Brian Potetz | 2 | 95 | 6.10 |
| Tai Sing Lee | 3 | 794 | 88.73 |