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Allow use of pairwise_bernoulli rule for connections to devices #3110

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heplesser opened this issue Feb 21, 2024 · 1 comment
Open

Allow use of pairwise_bernoulli rule for connections to devices #3110

heplesser opened this issue Feb 21, 2024 · 1 comment
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I: No breaking change Previously written code will work as before, no one should note anything changing (aside the fix) S: Normal Handle this with default priority stale Automatic marker for inactivity, please have another look here T: Enhancement New functionality, model or documentation

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@heplesser
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#560 prohibited the use of probabilistic connection rules when connecting to devices to address #351.

This rule is overly restrictive: Only connection rules that have a global constraint, such as fixed in- or outdegree and fixed total number. In contrast, pairwise Bernoulli checks each connection independently, so as long as each VP only considers sources on that VP, it is safe to use it for connecting to devices.

A use case is to randomly connect a given percentage of neurons in a population to a spike recorder for recording, when recording of all neurons is not practical. This would be especially useful for NEST Desktop. Note that slicing a subset of neurons is only sensible for random networks (mentioned by @babsey).

@heplesser heplesser added T: Enhancement New functionality, model or documentation S: Normal Handle this with default priority I: No breaking change Previously written code will work as before, no one should note anything changing (aside the fix) labels Feb 21, 2024
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Issue automatically marked stale!

@github-actions github-actions bot added the stale Automatic marker for inactivity, please have another look here label Apr 22, 2024
@jessica-mitchell jessica-mitchell moved this to To do (open issues) in Kernel Aug 6, 2024
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Labels
I: No breaking change Previously written code will work as before, no one should note anything changing (aside the fix) S: Normal Handle this with default priority stale Automatic marker for inactivity, please have another look here T: Enhancement New functionality, model or documentation
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