HCN_SU2015_DCN

class braincell.channel.HCN_SU2015_DCN(size, g_max=Quantity(0.01, 'mS / cm^2'), E=Quantity(-45., 'mV'), name=None)

H-current imported for the deep cerebellar nucleus (DCN) model.

Ports the single m gate (power 2) NEURON mechanism HCN_SU15_DCN.mod used in the deep-cerebellar-nucleus deposit of (Sudhakar et al., 2015) [2].

\[\begin{split}\begin{aligned} I_h &= g_{\mathrm{max}} \, m^2 \, (E - V) \\ m_\infty &= \frac{1}{1 + \exp((V + 80) / 5)} \\ \tau_m &= \frac{400}{\mathrm{qdeltat}} \end{aligned}\end{split}\]

\(\tau_m\) is a voltage-independent constant, not a function of \(V\); qdeltat is a fixed internal value (default 1.0) set in __init__ and is not exposed as a parameter.

Parameters:

• size (int | Sequence[int] | integer | Sequence[integer]) – Channel state shape.

• g_max (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable) – Maximal conductance density, default 0.01 mS/cm2.

• E (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable) – Reversal potential, default -45.0 mV.

• name (str | None) – Optional channel name.

Notes

This class overrides reversal_potential() to return self.E instead of an ion’s reversal potential. The m gate carries no q10/phi, so HH.gate_phi() resolves to the default 1.0.

Ported from HCN_SU15_DCN.mod. Its kinetics belong 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 Sudhakar et al. (2015) [2]. HCN is one of only four mechanism names (with CaLVA, NaP and SK) that occur as strings in the text of [2]; the paper does not, however, print the Boltzmann or time constants above for any mechanism – those are established only by direct comparison against the .mod source, not by a per-mechanism statement in either paper.

f_m_tau() returns the constant 400.0 / qdeltat (400 ms by default), a genuine, finite, voltage-independent time constant – the gate is not instantaneous. The former NEURON TABLE directive tabulated only minf over [-150, 100] mV; taum was never tabulated because it does not depend on voltage, not because it is zero or absent.

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] (1,2,3)

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

reversal_potential(V, *ions)

Return the reversal potential driving the current.

Parameters:

• V (ArrayLike) – Membrane potential. Unused by the default implementation; present so overrides can depend on it.

• *ions (IonInfo) – Ion states, in the declaration order of root_type.

Returns:

Reversal potential, as a voltage. Defaults to that of the first ion, which for a joint root type is the first entry of root_type.__args__. Override to read a fixed self.E or a non-leading ion.

Return type:

ArrayLike

root_type

alias of HHTypedNeuron