Release notes

NNMT 1.3.0

• The option to use a lognormal delay distribution has been added to the calcultion of the delay distribution matrix in nnmt.network_properties. As the characteristic function of a lognormal distribution has no closed analytic form, the numerical integration proposed in Beaulieu [2008] has been implemented.
• Add functions for computing the cvs of lif neurons: nnmt.lif.exp.cvs(), nnmt.lif.exp._cvs(), and nnmt.lif.exp._cvs_single_population().
• Add functions for computing pairwise_effective_connectivity in linear response approximation: nnmt.lif.exp.pairwise_effective_connectivity(), nnmt.lif.exp._pairwise_effective_connectivity().
• Add functions for computing the spectral bound of the pairwise effective connectivity matrix: nnmt.lif.exp.spectral_bound(), nnmt.lif.exp._spectral_bound().
• Add functions for computing the pairwise covariances in linear response approximation: nnmt.lif.exp.pairwise_covariances(), nnmt.lif.exp._pairwise_covariances().
• Add an example on how to use NNMT to predict the firing rates, CVs, and pair-wise covariances of a simple E-I network. The results are then compared to a NEST simulation.

NNMT 1.2.0

• Generalize firing rate integration: It is now possible to specify functions that have to be iterated with the firing rates in a dictionary including their input arguments. This allows a much more general usage of the function.
• Generalize input functions in lif.delta and lif.exp: They now simply pass on given arguments, such that future changes of functions in lif._general only affect the function in lif._general. Therefore, we introduced new utility functions that allow automatic extraction of required and optional parameters from network parameters and the functions to be executed.
• Allow usage of external input currents in calculation of mean_input and firing rates for lif neurons

NNMT 1.1.1

• Add Hahne et al. 2017 and Layer et al. 2022 to delta firing rate references.

NNMT 1.1.0

• Move firing rate integration procedure used for lif neurons from nnmt.lif._general to nnmt._solvers, where such general solving procedures are to be collected in the future.
• Add methods for binary neurons: - mean input - std input - mean activity - balanced threshold
• Add example comparing binary firing rates with simulation.
• Fix bug of fixture creation for lif neurons, which wouldn’t create all fixtures on passing all
• Move helper functions for lif fixture creation to own file.
• Add tests and fixture creation for binary neurons.

NNMT 1.0.2

• Fix calculation of mean input and std input for lif.exp. Previously tau_m was multiplied with the firing rates before the dot product with the connectivity. However, as tau_m is representing the post-synaptic membrane time constant, it should be multiplied after performing the dot product.
• Fix explanation of tau_m in docstrings.
• Add new integration test for lif.exp._firing_rates with vector parameters.
• Fix docopt usage in fixture creation for unit and integration fixtures.

NNMT 1.0.1

• Deepcopy parameter dictionaries on instantiation of network model. Otherwise dictionary items can change unwantedly if netork parameters are changed.
• Add approximations and assumptions to docstrings.
• Add explanation of approximations to docs.
• Add table of LIF parameters and NNMT variables to docs.
• Fix description in docstrings for tau_s.
• Fix typos in docstrings.
• Add new example of adjusting the low-gamma peak in the microcircuit model.
• Add pytest and pytest-mock to setup requirements, such that after pip installion the tests can be run.

NNMT 1.0.0

Initial release.