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)[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.
- Parameters:
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.