Spike Reliability
Measure trial reliability as at least one spike in a supplied inclusive response window; empty trials count as nonresponsive, not missing observations.
Description
Spike-response reliability is the fraction of trials containing at least one spike in a defined response window.
Spike-response reliability is the fraction of trials containing at least one spike in a defined response window. Multiple spikes in one trial still count as one responsive trial.
The tool returns responsiveTrialIndexes (responsive trial indexes); responseReliability (response reliability). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.
When to use Spike Reliability
Use Spike Reliability when you already have trialSpikeLatenciesMs, responseWindowStartMs, responseWindowEndMs and want responsiveTrialIndexes, responseReliability. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.
How to use Spike Reliability and understand its results
- Enter trialSpikeLatenciesMs, responseWindowStartMs, responseWindowEndMs using the field table below. Match the units, ordering and data boundaries described in the calculation.
- Run the tool after checking the stated bounds and matching paired observations or categories. A malformed request gives a tool error rather than a usable measurement.
- Read responsiveTrialIndexes, responseReliability. Use the calculation and interpretation below to distinguish amounts, ratios, signs and decision flags where applicable.
- Review the supplied parameters and assumptions before using the result in a report or comparing it with another observation.
| Input field | Meaning | Value type and bounds | Requirement |
|---|---|---|---|
| trialSpikeLatenciesMs | Trial spike latencies ms | list of list of number | Required |
| responseWindowStartMs | Response window start ms | number | Required |
| responseWindowEndMs | Response window end ms | number | Required |
Include both time boundaries; return responsive trial indexes in original order. Trial rows may be empty, but timestamps within each row must be strictly increasing.
reliability = responsiveTrialCount / trialCount The symbols refer to the supplied quantities or generated values described above; unit conversions and comparison boundaries follow the stated implementation.
{
"input": {
"trialSpikeLatenciesMs": [
[
2,
5
],
[
20
],
[
4
]
],
"responseWindowStartMs": 0,
"responseWindowEndMs": 10
},
"output": {
"responsiveTrialIndexes": [
0,
2
],
"responseReliability": 0.6666666666666666
}
}Limitations and assumptions
The window is supplied externally; spontaneous firing, detection errors and response significance are not modelled.
Numerical inputs must be finite. The tool checks its declared input shape and calculation constraints; those checks do not verify the quality, calibration or representativeness of the supplied observations.
Alternative or Complementary methods
Spike Timing Precision: Derive per-trial stimulus-aligned first-spike latencies and inverse sample SD precision; identical latencies have undefined infinite precision and return an error. Evoked Firing Rate: Compare paired trial spike counts over different baseline/response exposures and preserve signed net evoked rate, including suppression.
Methodological background: Spike train variability.1
References
-
Spike train variability — Gerstner et al.; Neuronal Dynamics
Similar or alternative tools
- Spike Timing Precision
Derive per-trial stimulus-aligned first-spike latencies and inverse sample SD precision; identical latencies have undefined infinite precision and return an error.
- Evoked Firing Rate
Compare paired trial spike counts over different baseline/response exposures and preserve signed net evoked rate, including suppression.