Baseline Firing Rate Calculator
Actual pre-stimulus baseline 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 baseline firing rate requires independently selected pre-stimulus windows.
A baseline firing rate requires independently selected pre-stimulus windows.1
This tool counts supplied spikes over the union of those windows without assigning the baseline label from the rate itself.
When to use Baseline Firing Rate Calculator
This tool counts supplied spikes over the union of those windows without assigning the baseline label from the rate itself.
How to use Baseline 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 Baseline 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 |
{
"input": {
"actualSpikeTimesSeconds": [
0.5,
1.5,
2.5
],
"callerQualifiedWindowStartsSeconds": [
0,
2
],
"callerQualifiedWindowEndsSeconds": [
1,
3
],
"callerQualifiedProtocolContext": "pre-stimulus baseline 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 Tonic Firing Rate Calculator for a complementary operation: A tonic firing-rate observation requires independently classified recording windows rather than a label inferred from the rate alone.
Use Spontaneous Firing Rate Calculator for a complementary operation: Spontaneous firing rate requires independently identified no-stimulus recording windows.
References
-
Baseline Firing Rate Calculator — tool.io
Similar or alternative tools
- Tonic Firing Rate Calculator
A tonic firing-rate observation requires independently classified recording windows rather than a label inferred from the rate alone.
- Spontaneous Firing Rate Calculator
Spontaneous firing rate requires independently identified no-stimulus recording windows.