CdpHVA_SU2015_DCN

class braincell.ion.CdpHVA_SU2015_DCN(size, temp=Quantity(309.15, 'K'), kCa=Quantity(3.45e-07, '1 / C'), tauCa=Quantity(70., 'ms'), caiBase=Quantity(5.e-05, 'mM'), depth=Quantity(0.2, 'um'), Co=None, Ci_initializer=None, name=None, **channels)

HVA-current-driven calcium pool for the deep cerebellar nuclei.

First-order relaxation model for the calcium pool associated with high-voltage-activated (HVA) calcium channels in deep cerebellar nucleus (DCN) neurons: the current drive from attached HVA channels pushes Ci away from a fixed baseline, and a single time constant relaxes it back.

Parameters:

• size (int | Sequence[int] | integer | Sequence[integer]) – The size of the simulation target, typically the number of neurons or compartments. Forwarded unchanged to Calcium.

• temp (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable) – Absolute temperature used by the Nernst equation in E. Defaults to 36 degrees Celsius, converted to kelvin via u.celsius2kelvin before being stored.

• kCa (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable) – Current-to-concentration scale factor in derivative(). Defaults to 3.45e-7 /coulomb.

• tauCa (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable) – Relaxation time constant toward caiBase in derivative(). Defaults to 70.0 ms.

• caiBase (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable) – Baseline calcium concentration that Ci relaxes toward, and the default value of Ci_initializer when none is given. Defaults to 50e-6 mM.

• depth (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable) – Shell depth dividing the current-drive term in derivative(). Defaults to 0.2 um.

• Co (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable | None) – Extracellular calcium concentration. Defaults to None, which falls back to Calcium.default_Co inside DynamicNernstIon._init_dynamic_nernst_ion().

• Ci_initializer (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable | None) – Initializer for the dynamic Ci state. Defaults to None, which falls back to a constant initializer at caiBase.

• name (str | None) – Runtime ion instance name. Defaults to None.

• **channels – Channel instances to attach to this ion, forwarded unchanged to Calcium.

Raises:

ValueError – If temp is explicitly passed as None. DynamicNernstIon._init_dynamic_nernst_ion() requires an explicit temperature and does not fall back to a class default.

See also

Calcium

Base calcium ion family this class computes a reversal potential for.

CdpLVA_SU2015_DCN

Sibling DCN calcium pool with an identical relaxation model driven by low-voltage-activated current.

braincell.ion._base.DynamicNernstIon

Mixin this class builds on; documents the Ci/Co/E interface shared by all dynamic calcium ions.

Notes

This class subclasses DynamicNernstIon, not KineticIon: it defines a single derivative() method rather than declarative Factor/Species/Reaction tuples, unlike the other Cdp* mechanisms in this module.

Ported from DCN/ion/CdpHVA_SU15_DCN.mod. derivative() implements

\[\frac{dCi}{dt} = -\frac{kCa}{depth} \cdot I_{total} \cdot 10^4 - \frac{Ci - caiBase}{tauCa}\]

NEURON’s raw ica is efflux-positive; BrainCell channel currents follow the repo-wide inward-positive convention, so the sign of the current-drive term is flipped relative to the original NMODL expression to keep a positive total_current increasing Ci. When no channels are attached, total_current defaults to zero and derivative() reduces to pure relaxation toward caiBase. The current-drive term is computed from unitless decimals (via to_decimal) rather than through brainunit dimensional arithmetic, matching the imported NMODL’s * 1e4 unit-conversion literal rather than deriving it dimensionally.

The kinetic constants (kCa, tauCa, caiBase, depth) are DCN-model parameters; no paper’s text can be cited as printing this specific relaxation-model form. The kinetics trace to the deep cerebellar nucleus model of Steuber et al. (2011) [1], translated from GENESIS to NEURON by Luthman et al. (2011) and used in the NEURON DCN model of Sudhakar et al. (2015) [2], from which this mechanism is imported.

References

[1]

Steuber, V., Schultheiss, N. W., Silver, R. A., De Schutter, E., & Jaeger, D. (2011). Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells. Journal of Computational Neuroscience, 30(3), 633-658. doi:10.1007/s10827-010-0282-z

[2]

Sudhakar, S. K., Torben-Nielsen, B., & De Schutter, E. (2015). Cerebellar nuclear neurons use time and rate coding to transmit Purkinje neuron pauses. PLOS Computational Biology, 11(12), e1004641. doi:10.1371/journal.pcbi.1004641

derivative(Ci, V, total_current=None)

Return dCi/dt for the concrete dynamic ion model.

uses_total_current = True

When true, the template precomputes the aggregate ion current and passes it to derivative(..., total_current=...).