Tonic Firing Rate Calculator

Actual independently classified tonic qualified spike count over independently classified half open windows UNION; overlaps no double count. Does not infer baseline/spontaneous/tonic/phasic labels from rate, bin ned peak or burst pattern. Protocol/window classification actual external evidence, not automatic neural phenotype.

Description

A tonic firing-rate observation requires independently classified recording windows rather than a label inferred from the rate alone.

A tonic firing-rate observation requires independently classified recording windows rather than a label inferred from the rate alone.^[1]

This tool counts supplied spikes over the union of those windows and reports spikes per second.

When to use Tonic Firing Rate Calculator

This tool counts supplied spikes over the union of those windows and reports spikes per second.

How to use Tonic Firing Rate Calculator and how it works

Prepare spike times seconds, qualified window starts seconds, qualified window ends seconds, qualified protocol context, using the input definitions and matching units below.
Enter the required fields and run Tonic Firing Rate Calculator.
Read result.actualQualifiedSpikeCount, result.actualQualifiedExposureSeconds, result.observedConditionalRatePerSecond using the units, signs and ordering explained below.
Correct any rejected inputs and rerun; interpret the result within the stated model and its assumptions.

Input field	Meaning	Value type	Requirement
actualSpikeTimesSeconds	Nonnegative distinct spike timestamps in increasing order; an empty spike list is allowed.	array of number	Required; other constraints below
callerQualifiedWindowStartsSeconds	Start-inclusive recording-window coordinates aligned with the end list.	array of number	Required; other constraints below
callerQualifiedWindowEndsSeconds	End-exclusive recording-window coordinates aligned with the start list.	array of number	Required; other constraints below
callerQualifiedProtocolContext	Nonblank externally established protocol and window-classification context.	string	Required; other constraints below

Require a nonblank independently qualified protocol context and aligned nonempty window start/end arrays. Validate ordered endpoint pairs, sort and merge overlapping or touching half-open windows. Require supplied spike times to be nonnegative, distinct and strictly increasing; the spike list may be empty. Count each spike lying on or after a merged start and strictly before its end once. Require positive total exposure, then return qualified count, exposure seconds and count/exposure in Hz. The code does not independently enforce nonnegative window endpoints or infer the window classification.

count=sum(spikeInsideUnion); exposureSeconds=sum(mergedEnd-mergedStart); Hz=count/exposureSeconds

Apply the input ordering, units and branch conditions stated above.

Output field	Meaning	Value type
result	Object containing the calculated or selected fields below.	object
result.actualQualifiedSpikeCount	Number of supplied spikes inside the merged qualified half-open window union.	number
result.actualQualifiedExposureSeconds	Total duration of the merged qualified windows in seconds.	number
result.observedConditionalRatePerSecond	Qualified spike count divided by union exposure seconds, in Hz.	number

json

{
  "input": {
    "actualSpikeTimesSeconds": [
      0.5,
      1.5,
      2.5
    ],
    "callerQualifiedWindowStartsSeconds": [
      0,
      2
    ],
    "callerQualifiedWindowEndsSeconds": [
      1,
      3
    ],
    "callerQualifiedProtocolContext": "independently classified tonic actual observed exposure/windows"
  },
  "output": {
    "result": {
      "actualQualifiedSpikeCount": 2,
      "actualQualifiedExposureSeconds": 2,
      "observedConditionalRatePerSecond": 1
    }
  }
}

Limitations and assumptions

The calculation uses the supplied observations and model inputs. It does not collect measurements, select a reference population or coefficient, or establish that the model applies to the actual case. The stated branch conditions and output units remain part of the result.

All fields require explicit values. Unexpected fields, wrong types, nonfinite numbers and sparse arrays are rejected. Alignment, bounds and supported-token rules are explained above; extremely large inputs or nonfinite results can also fail. Input errors differ from successful false comparisons, unavailable-domain flags and empty match lists.

Alternative or Complementary methods

Use Spontaneous Firing Rate Calculator for a complementary operation: Spontaneous firing rate requires independently identified no-stimulus recording windows.

Use Phasic Firing Rate Calculator for a complementary operation: A phasic firing-rate observation needs independently identified stimulus-locked windows.

References

Tonic Firing Rate Calculator — tool.io

Similar or alternative tools

Spontaneous Firing Rate Calculator
Spontaneous firing rate requires independently identified no-stimulus recording windows.
Phasic Firing Rate Calculator
A phasic firing-rate observation needs independently identified stimulus-locked windows.