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.
Description
Interspike intervals are elapsed times between consecutive spikes in one identified train.
Interspike intervals are elapsed times between consecutive spikes in one identified train. Interval calculation begins after spikes have already been assigned to a source.1
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.
When to use Spike Train Interspike Intervals
Use this tool to 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.
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 Train Interspike Intervals 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 spikeTimesMs using the field table and calculation rules, then run the tool.
- Read intervalsMs. 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 |
|---|---|---|---|
| spikeTimesMs | Strictly increasing spike timestamps. (ms) | list of number; minimum entries: 2; maximum entries: 100000 | Required |
Enter two through 100,000 finite timestamps in milliseconds, strictly increasing. Return n−1 positive intervals in chronological order. Negative timestamps relative to a shared origin are allowed.
interval_i=spikeTime_(i+1)−spikeTime_i Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| intervalsMs | Consecutive timestamp differences. (ms) | list of number; strictly above: 0; minimum entries: 1; maximum entries: 100000 |
{
"input": {
"spikeTimesMs": [
5,
15,
40,
55
]
},
"output": {
"intervalsMs": [
10,
25,
15
]
}
}Limitations and assumptions
The tool does not perform spike sorting, detect spikes or infer refractory violations. Equal and reversed timestamps are input errors rather than zero or negative intervals.
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 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. For a complementary task, use 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.
References
-
Spike Train Interspike Intervals — tool.io
Similar or alternative tools
- 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.
- 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.