Adaptation Ratio

Compare late versus early rates derived from disjoint groups of consecutive spike intervals; ratio below one indicates slowing, not a fitted adaptation mechanism.

Description

Firing adaptation can be summarized by comparing early and late interspike intervals.

Firing adaptation can be summarized by comparing early and late interspike intervals. Since rate is reciprocal interval, their order reverses in a late-to-early rate ratio.

The tool returns earlyMeanIsiMs (early mean isi ms); lateMeanIsiMs (late mean isi ms); lateEarlyRateRatio (late early rate ratio). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.

When to use Adaptation Ratio

Use Adaptation Ratio when you already have spikeTimesMs, intervalsPerEnd and want earlyMeanIsiMs, lateMeanIsiMs, lateEarlyRateRatio. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.

How to use Adaptation Ratio and understand its results

Enter spikeTimesMs, intervalsPerEnd 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 earlyMeanIsiMs, lateMeanIsiMs, lateEarlyRateRatio. 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
spikeTimesMs	Spike times ms	list of number	Required
intervalsPerEnd	Intervals per end	integer	Required

Use strictly increasing spike times and equally sized disjoint end groups selected by a positive integer interval count.

late/early rate = mean(early ISIs)/mean(late ISIs)

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

json

{
  "input": {
    "spikeTimesMs": [
      0,
      10,
      30,
      70
    ],
    "intervalsPerEnd": 1
  },
  "output": {
    "earlyMeanIsiMs": 10,
    "lateMeanIsiMs": 40,
    "lateEarlyRateRatio": 0.25
  }
}

Limitations and assumptions

Values below one indicate slower late firing under this convention. The method does not fit an adaptation time course or classify causes.

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

Coefficient of Variation ISI: Derive consecutive interspike intervals from ordered timestamps and calculate sample CV; differs from a generic CV of supplied measurements. Burst Index: Classify maximal spike runs using a supplied ISI gap and minimum burst length; report the fraction of spikes assigned to bursts, not a universal burst-index definition.

Methodological background: Spike train variability.^[1]

References

Spike train variability — Gerstner et al.; Neuronal Dynamics

Similar or alternative tools

Coefficient of Variation ISI
Derive consecutive interspike intervals from ordered timestamps and calculate sample CV; differs from a generic CV of supplied measurements.
Burst Index
Classify maximal spike runs using a supplied ISI gap and minimum burst length; report the fraction of spikes assigned to bursts, not a universal burst-index definition.