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)[source]#

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.

  • E (float, ArrayType) – The reversal potential for the synaptic current. [mV]

  • alpha (float, ArrayType) – Binding constant. Default 0.062

  • beta (float, ArrayType) – Unbinding constant. Default 3.57

  • cc_Mg (float, ArrayType) – Concentration of Magnesium ion. Default 1.2 [mM].

  • sharding (sequence of str) – The axis names for variable for parallelization.

  • name (str) – The model name.

update(conductance, potential)[source]#

The function to specify the updating rule.