Add Ornstein–Uhlenbeck noise generator

[FinnBurkhardt]

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

$$ dx = \frac{1}{\tau}(\mu - x)\ dt + \sigma\ dW $$

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

  • Internally integrates the OU equation at user-specified dt.
  • On each simulation step it emits the current (x) to connected targets.

Tests (test_ou_noise_generator.py)

• Parameter setting and defaults

  • Verifies that set() and SetDefaults() both correctly configure all parameters (mean, std, tau, dt).

• Error conditions

  • Asserts that attempting to create the generator with a dt not divisible by nest.resolution raises StepMultipleRequired.

• Mean and variance

  • Runs 100 trials of 1000 ms each, records the last current value, and checks that the sample mean and variance match the target $\mu$ and $\sigma^2$ within reasonable statistical bounds.

• Autocorrelation

  • Collects a long trace, discards the initial $10\tau$ burn-in, and computes the empirical lag-1 autocorrelation.
  • Confirms it matches $\exp(-\text{dt}/\tau)$ within a small tolerance.

• Cross-correlation

  • Connects a single OU generator to two independent neurons and records their membrane potentials.
  • Computes the cross-correlation of the two voltage traces and verifies it remains near zero.

[heplesser]

@FinnBurkhardt Would you be able to address the open issues within a few days? I also added two minor suggestions. Please merge master into your branch for this PR to ensure that the testsuite works properly.

[FinnBurkhardt]

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?

Thanks, I’ve applied the suggested changes.

For examples, I’d have two candidates at hand:

1. Single neuron driven by the OU noise generator.

2. White vs. OU noise (matched mean/variance), comparing firing rate and coefficient of variation.

Would one (or both) be useful for the docs?

[jessica-mitchell]

@FinnBurkhardt if they are ready then we could take both. You can add them to pynest/examples/ (please adhere to the style of docstring as in the other examples)

And you can add it to the doc/htmldoc/examples/index.rst page: I think in the 'stimulating network' section would make sense?

    .. grid-item-card:: Stimulating networks
           :img-top: ../static/img/pynest/sensitivitypertubation.png

           * :doc:`../auto_examples/sinusoidal_poisson_generator`
           * :doc:`../auto_examples/sinusoidal_gamma_generator`
           * :doc:`../auto_examples/repeated_stimulation`
           * :doc:`../auto_examples/sensitivity_to_perturbation`
           * :doc:`../auto_examples/intrinsic_currents_spiking`
           * :doc:`../auto_examples/intrinsic_currents_subthreshold`

It also needs to be added to the .. toctree::

I can also help with this if it's confusing!