Kca1p1_MA2020_GrC

Kca1p1_MA2020_GrC#

class braincell.channel.Kca1p1_MA2020_GrC(size, g_max=Quantity(10., 'mS / cm^2'), q10_base=3.0, temp=Quantity(295.15, 'K'), name=None, solver=None, substeps=None)#

Kca1.1 Ca- and voltage-activated K current, granule-cell variant.

The same ten-state Kca1.1 Markov scheme documented in Kca1p1_MA2020_GoC – parameters from the allosteric BK-channel gating scheme of (Cox, Cui, & Aldrich, 1997) [1] as adapted by (Anwar, Hong, & De Schutter, 2012) [2] – reused unchanged for the cerebellar granule cell subtype model of (Masoli et al., 2020) [3]. This class is a Python subclass of Kca1p1_MA2020_GoC, but that inheritance relationship is purely a code-reuse device: the kinetics and model citation below belong to the granule cell paper, not to the Golgi cell paper cited on the base class.

Parameters:

See also

Kca1p1_MA2020_GoC

The Python base class supplying the shared state topology and rate equations; cites the Golgi cell paper, not the granule cell paper cited here.

Kca1p1_MA2025_BC

Same kinetics, basket-cell model citation.

Kca1p1_MA2024_PC

Same kinetics, Purkinje-cell model citation.

Kca1p1_RI2021_SC

Same kinetics, stellate-cell model citation.

Notes

Ported from Kca1p1_MA20_GrC.mod. This class does not override __init__: the constructor, state topology, and rate methods are all inherited unchanged from Kca1p1_MA2020_GoC. Only the register_channel key and this docstring’s model citation differ. Despite the Python inheritance from the Golgi cell class, the correct model citation is the granule cell paper below – the two cerebellar cell-type papers were published together and this citation is not interchangeable with the one on Kca1p1_MA2020_GoC.

References