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CHANGELOG.md

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v0.5.10

  • models now have the parameter sampling_dt which will downsample the output to a specified step size (in ms)
  • loadData: add subject-wise length matrices ds.Dmats

v0.5.9

  • ALN model added to the multimodel framework
  • ThalamicMassModel now works with autochunk for very long simulations with minimal RAM usage!

v0.5.8

  • Hotfix: include pypet_logging.ini in pypi package
  • Evolution: new method getIndividualFromHistory()

v0.5.7

  • example-0.5: Demonstrating the use of external stimuli on brain networks
  • example-1.3: 2D bifurcation diagrams using pypet
  • bold: BOLD numerical overflow bug fixed
  • evolution: dfEvolution and dfPop fix
  • exploration: fix seed for random initial conditions
  • various minor bugfixes

v0.5.5

  • Hotfix for RNG seed in exploration: Seed None is now converted to "None" for for pypet compatibility only when saving the model.params to the trajectory.
  • Fix: dfEvolution drops duplicate entries from the evolution.history.

v0.5.4

  • New function func.construct_stimulus()
  • New example of stimulus usage: examples/example-0.5-aln-external-stimulus.ipynb
  • Fixed RNG seed bug, where the seed value None was converted to 0 (because of pypet) and lead to a predictable random number generator

v0.5.3

  • ALNModel now records adaptation currents! Accessible via model.outputs.IA

v0.5.1

Evolution:

  • NSGA-2 algorithm implemented (Deb et al. 2002)
  • Preselect complete algorithms (using algorithm="adaptive" or "nsga2")
  • Implement custom operators for all evolutionary operations
  • Keep track of the evolution history using evolution.history
  • Genealogy evolution.tree available from evolution.buildEvolutionTree() that is networkx compatible [1]
  • Continue working: saveEvolution() and loadEvolution() can load an evolution from another session [2]
  • Overview dataframe evolution.dfPop now has all fitness values as well
  • Get scores using getScores()
  • Plot evolution progress with evolutionaryUtils.plotProgress()

Exploration:

  • Use loadResults(all=True) to load all simulated results from disk to memory (available as .results) or use all=False to load runs individually from hdf. Both options populate dfResults.
  • loadResults() has memory cap to avoid filling up RAM
  • loadDfResults() creates the parameter table from a previous simulation
  • explorationUtils.plotExplorationResults() for plotting 2D slices of the explored results with some advanced functions like alpha maps and contours for predefined regions.

devUtils

  • A module that we are using for development and research with some nice features. Please do not rely on this file since there might be breaking changes in the future.
    • plot_outputs() like a true numerical simlord
    • model_fit() to compute the model's FC and FCD fit to the dataset, could be usefull for everyone
    • getPowerSpectrum() does what is says
    • getMeanPowerSpectrum() same
    • a very neat rolling_window() from a numpy PR that never got accepted

Other:

  • Data loading:
    • Dataset can load different SC matrix normalizations: "max", "waytotal", "nvoxel"
    • Can precompute FCD matrices to avoid having to do it later (fcd=True)
  • neurolib/utils/atlas.py added with aal2 region names (thanks @jajcayn) and coordinates of centers of regions (from scans of @caglorithm's brain 🤯)
  • ParameterSpace has .lowerBound and .upperBound.
  • pypet finally doesn't create a billion log files anymore due to a custom log config

v0.5.0

  • New model: Thalamus model ThalamicMassModel (thanks to @jajcayn)
    • Model by Costa et al. 2016, PLOS Computational Biology
  • New tools for parameter exploration: explorationUtils.py aka eu
    • Postprocessing of exploration results using eu.processExplorationResults()
    • Find parameters of explored simulations using eu.findCloseResults()
    • Plot exploration results via eu.plotExplorationResults() (see example image below)
  • Custom transformation of the inputs to the BOLDModel.
    • This is particularly handy for phenomenological models (such as FHNModel, HopfModel and WCModel) which do not produce firing rate outputs with units in Hz.
  • Improvements
    • Models can now generate random initial conditions using model.randomICs()
    • model.params['bold'] = True forces BOLD simulation
    • BoxSearch class: search.run() passes arguments to model.run()
    • BOLD output time array renamed to t_BOLD

v0.4.1

  • New model: Wilson-Cowan neural mass model implemented (thanks to @ChristophMetzner )
  • Simulations now start their output at t=dt (as opposed to t=0 before). Everything before is now considered an initial condition.
  • Fix: Running a simulation chunkwise (using model.run(chunkwise=True)) and normally (using model.run()) produces output of the same length
  • Fix: aln network coupling, which apparent when simulating chunkwise with model.run(chunkwise=True, chunksize=1)
  • Fix: Correct use of seed for RNG
  • Fix: Matrices are not normalized to max-1 anymore before each run.
  • Fix: Kolmogorov distance of FCD matrices and timeseries