MgBlock

class brainpy.dyn.MgBlock(E=0.0, cc_Mg=1.2, alpha=0.062, beta=3.57, V_offset=0.0, sharding=None, name=None, scaling=None)

Synaptic output based on Magnesium blocking.

Given the synaptic conductance, the model output the post-synaptic current with

\[I_{syn}(t) = g_{\mathrm{syn}}(t) (E - V(t)) g_{\infty}(V,[{Mg}^{2+}]_{o})\]

where The fraction of channels \(g_{\infty}\) that are not blocked by magnesium can be fitted to

\[g_{\infty}(V,[{Mg}^{2+}]_{o}) = (1+{e}^{-\alpha V} \frac{[{Mg}^{2+}]_{o}} {\beta})^{-1}\]

Here \([{Mg}^{2+}]_{o}\) is the extracellular magnesium concentration.

Parameters:

• E (Union[float, TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray)]) – The reversal potential for the synaptic current. [mV]

• alpha (Union[float, TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray)]) – Binding constant. Default 0.062

• beta (Union[float, TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray)]) – Unbinding constant. Default 3.57

• cc_Mg (Union[float, TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray)]) – Concentration of Magnesium ion. Default 1.2 [mM].

• sharding (Optional[Sequence[str]]) – The axis names for variable for parallelization.

• name (Optional[str]) – The model name.

update(conductance, potential)

The function to specify the updating rule.