Spike Count Fano Factor
Calculate the empirical Fano factor as population variance (divisor n) divided by mean spike count across equal-duration repeated trials. The caller supplies counts for one neuron; a zero mean is undefined.
Description
Spike-count Fano factor relates across-trial count variance to the mean.
Spike-count Fano factor relates across-trial count variance to the mean. Comparable equal-duration trials for one neuron define its statistical basis.1
Calculate the empirical Fano factor as population variance (divisor n) divided by mean spike count across equal-duration repeated trials. The caller supplies counts for one neuron; a zero mean is undefined.
When to use Spike Count Fano Factor
Use this tool to calculate the empirical Fano factor as population variance (divisor n) divided by mean spike count across equal-duration repeated trials. The caller supplies counts for one neuron; a zero mean is undefined.
Prepare the task-specific rates, comparable response-time means, identified spike train, processed ERP or estimated spectra requested. Confirm units, preprocessing, sample pairing and caller-fitted model coefficients.
How to use Spike Count Fano Factor and understand its results
- Prepare the task-specific rates, comparable response-time means, identified spike train, processed ERP or estimated spectra requested. Confirm units, preprocessing, sample pairing and caller-fitted model coefficients.
- Enter spikeCounts using the field table and calculation rules, then run the tool.
- Read meanCount, populationVariance, fanoFactor. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| spikeCounts | Nonnegative spike counts across equal-duration trials. | list of integer; minimum: 0; minimum entries: 2; maximum entries: 100000 | Required |
Enter two through 100,000 nonnegative integer counts. Use an online mean/second-moment update, divide variance by n and floor tiny negative variance at zero. Reject all-zero counts because the mean denominator is zero.
mean=Σcounts/n; populationVariance=Σ(count−mean)²/n; Fano=variance/mean Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| meanCount | Mean trial spike count. | number; strictly above: 0 |
| populationVariance | Trial count variance using divisor n. | number; minimum: 0 |
| fanoFactor | Population variance divided by mean. | number; minimum: 0 |
{
"input": {
"spikeCounts": [
0,
4
]
},
"output": {
"meanCount": 2,
"populationVariance": 4,
"fanoFactor": 2
}
}Limitations and assumptions
This is population-divisor empirical variance, not n−1 sample variance. Trial stationarity, equal duration and neuron identity are not checked, and uncertainty is not estimated.
Numeric inputs must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.
Alternative or Complementary methods
Use Spike Train Interspike Intervals: Calculate consecutive interspike intervals from strictly increasing timestamps for a single identified spike train. This does not assign spikes to neurons or perform spike sorting. For a complementary task, use Interspike Local Variation: Calculate Shinomoto local variation Lv = 3/(n−1) × sum(((ISI[i]−ISI[i+1])/(ISI[i]+ISI[i+1]))²) for positive consecutive intervals. This is the original Lv without a refractory-period correction.
References
-
Spike Count Fano Factor — tool.io
Similar or alternative tools
- Spike Train Interspike Intervals
Calculate consecutive interspike intervals from strictly increasing timestamps for a single identified spike train. This does not assign spikes to neurons or perform spike sorting.
- Interspike Local Variation
Calculate Shinomoto local variation Lv = 3/(n−1) × sum(((ISI[i]−ISI[i+1])/(ISI[i]+ISI[i+1]))²) for positive consecutive intervals. This is the original Lv without a refractory-period correction.