Description
Adaptive linear-nonlinear (aLN) mean-field model of a network of adaptive exponential integrate-and-fire (AdEx) neurons, comprising one excitatory (E) and one inhibitory (I) population. Originally developed by Augustin, Ladenbauer, Liebe & Obermayer (2017) and used as the whole-brain mean-field in neurolib by Cakan, Jajcay & Obermayer (2021).
The legacy tvb-o.owl ontology referred to this model by the short name ``Cortical`` (suffix ``_CO``); ``iri`` is preserved for backward ontology mapping while ``name`` reflects the canonical attribution.
Defaults are taken verbatim from ``neurolib/models/aln/loadDefaultParams.py``. Note that the full aLN dynamics require a precomputed transfer-function lookup table (``aln-precalc/quantities_cascade.h5``) that maps the filtered mean input ``mu`` and the membrane-potential standard deviation ``sigma`` to a steady-state firing rate. The state-variable equations below are the structural filtered-mean-input scaffold; the nonlinear transfer function ``r_E = Phi(mu_E, sigma_E)`` cannot be expressed in YAML and is therefore approximated as a sigmoid for backend code generation.