Table 2.

This table assumed that the designer has already selected a value of R for the subnetwork.

Operation	Components	Constraints and useful relations	Free parameters
Differentiation	Neuron 1	Cm,1 < Cm,2	τd
	Neuron 2	Cm,2 = τd	kd
		Cm,1 = Cm,2 − kd
	Syn. 1,	ksyn = 1/kd	ΔEs,1, maximize
	transmission	${\mathit{g}}_{\mathit{s}\text{,1}}=\frac{{\mathit{k}}_{\mathit{syn}}\cdot \mathit{R}}{\mathrm{\Delta }{\mathit{E}}_{\mathit{s}\text{,1}}-{\mathit{k}}_{\mathit{syn}}\cdot \mathit{R}}$
		ΔEs,1 − ksyn⋅R > 0
	Syn. 2,	${\mathit{g}}_{\mathit{s}\text{,2}}=\frac{\mathrm{\Delta }{\mathit{E}}_{\mathit{s}\text{,1}}}{\mathrm{\Delta }{\mathit{E}}_{\mathit{s}\text{,2}}}\cdot \frac{-{\mathit{k}}_{\mathit{syn}}\cdot \mathit{R}}{\mathrm{\Delta }{\mathit{E}}_{\mathit{s}\text{,1}}-{\mathit{k}}_{\mathit{syn}}\cdot \mathit{R}}$	ΔEs,2, minimize
	transmission
Integration	Neuron 1	Iapp,1 = R	ki,mean
		${\mathit{C}}_{\mathit{m}\text{,1}}=\frac{\text{1}}{\text{2}\cdot {\mathit{k}}_{\mathit{i},\text{mean}}}$
	Neuron 2	Iapp,2 = R
		Cm,1 = Cm,2
	Syn. 1,	$\mathrm{\Delta }{\mathit{E}}_{\mathit{s}\text{,1}}=\frac{-\mathit{R}}{{\mathit{g}}_{\mathit{s}\text{,1}}}$	ki,range
	transmission	${\mathit{g}}_{\mathit{s}\text{,1}}=\frac{\text{2}\cdot {\mathit{C}}_{\mathit{m}\text{,1}}}{\text{1}∕{\mathit{k}}_{\mathit{i}\text{,range}}-{\mathit{C}}_{\mathit{m}\text{,1}}}$
	Syn. 2,	gs,2 = gs,1
	transmission	ΔEs,2 = ΔEs,1

In this table, “minimize” refers to making a value as negative as possible and “maximize” refers to making a value as positive as possible.