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Add Ornstein–Uhlenbeck noise generator #3541
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FinnBurkhardt
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jessica-mitchell
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Aug 11, 2025
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Thanks for the contribution!
I have a minor change for the docs, and a question if there is a good use case we can use an example for this generator?
clinssen
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Aug 25, 2025
The correlation time constant :math:`\tau` of the process (ms). | ||
|
||
dt | ||
The interval :math:`\delta` between updates of the noise current (ms). |
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Suggested change
The interval :math:`\delta` between updates of the noise current (ms). | |
The time interval between updates of the noise current (ms). |
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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
T: Enhancement
New functionality, model or documentation
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Add Ornstein–Uhlenbeck Noise Generator
This pull request adds a new
ou_noise_generator
device and corresponding pytest-based tests. The device generates temporally correlated current following an Ornstein–Uhlenbeck (OU). The tests verify both parameter handling and statistical properties.Model Implementation
Files added
models/ou_noise_generator.h
models/ou_noise_generator.cpp
pytests/sli2py_stimulating/test_ou_noise_generator.py
Process equation
The state variable (x) evolves as
where
($\mu$ ) (
mean
parameter) is the long-term mean of the process,($\sigma$ ) (
std
parameter) its stationary standard deviation,($\tau$ ) (
tau
parameter) is the time constant,dt
is the internal integration step.Integration and output
dt
.Tests (
test_ou_noise_generator.py
)Parameter setting and defaults
set()
andSetDefaults()
both correctly configure all parameters (mean
,std
,tau
,dt
).Error conditions
dt
not divisible bynest.resolution
raisesStepMultipleRequired
.Mean and variance
Autocorrelation
Cross-correlation