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.

Description

Trial-to-trial first-spike latency variability describes temporal consistency after a stimulus.

Trial-to-trial first-spike latency variability describes temporal consistency after a stimulus. Here precision is defined as the reciprocal of the sample standard deviation.

The tool returns latenciesMs (latencies ms); latencySampleSdMs (latency sample sd ms); precisionInverseMs (precision inverse ms). 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 Timing Precision

Use Spike Timing Precision when you already have firstSpikeTimesMs, stimulusTimesMs and want latenciesMs, latencySampleSdMs, precisionInverseMs. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.

How to use Spike Timing Precision and understand its results

Enter firstSpikeTimesMs, stimulusTimesMs 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 latenciesMs, latencySampleSdMs, precisionInverseMs. 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
firstSpikeTimesMs	First spike times ms	list of number	Required
stimulusTimesMs	Stimulus times ms	list of number	Required

Subtract paired timestamps, require nonnegative latency and at least two observations with positive sample variance, and report SD and inverse-ms precision.

latency = firstSpike−stimulus; precision = 1/sampleSD(latencies)

The symbols refer to the supplied quantities or generated values described above; unit conversions and comparison boundaries follow the stated implementation.

json

{
  "input": {
    "firstSpikeTimesMs": [
      11,
      12,
      13
    ],
    "stimulusTimesMs": [
      10,
      10,
      10
    ]
  },
  "output": {
    "latenciesMs": [
      1,
      2,
      3
    ],
    "latencySampleSdMs": 1,
    "precisionInverseMs": 1
  }
}

Limitations and assumptions

Identical latencies are rejected because the reciprocal would be infinite. Missing-spike trials and measurement uncertainty are not handled.

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 Reliability: Measure trial reliability as at least one spike in a supplied inclusive response window; empty trials count as nonresponsive, not missing observations. Coefficient of Variation ISI: Derive consecutive interspike intervals from ordered timestamps and calculate sample CV; differs from a generic CV of supplied measurements.

Methodological background: Measures of scale: sample variance and standard deviation.^[1]

References

Measures of scale: sample variance and standard deviation — NIST/SEMATECH

Similar or alternative tools

Spike Reliability
Measure trial reliability as at least one spike in a supplied inclusive response window; empty trials count as nonresponsive, not missing observations.
Coefficient of Variation ISI
Derive consecutive interspike intervals from ordered timestamps and calculate sample CV; differs from a generic CV of supplied measurements.