Appendix 1—table 2. Description of the network model used in the error-driven learning task (4.6).

A model summary
Populations		3
Topology		—
Connectivity		Feedforward with all-to-all connections
Neuron model		Leaky integrate-and-fire (LIF) with exponential post-synaptic currents
Plasticity		Error-driven
Measurements		Spikes, membrane potentials
B populations
Name	Elements	Size
Input	Spike generators with pre-defined spike trains (see 4.6)	$N$
Teacher	LIF neuron	1
Student	LIF neuron	1
C connectivity
Source	Target	Pattern
Input	Teacher	All-to-all; synaptic delay $d$; random weights $w\sim 𝒰[w_{\text{min}},w_{\text{max}}]$; weights randomly shifted by $w_{\text{shift}}$ on each trial
Input	Student	All-to-all; synaptic delay $d$; fixed initial weights w0
D neuron model
Type		LIF neuron with exponential post-synaptic currents
Subthreshold dynamics		$\frac{\mathrm{d}u(t)}{\mathrm{d}t}=-\frac{u(t)-E_{\mathrm{L}}}{{\tau }_{\mathrm{m}}}+\frac{I_{\mathrm{s}}(t)}{C_{\mathrm{m}}}$$I_{\mathrm{s}}(t)={\sum }_{i,k}{J}_k{e}^{-(t-t_i^k)/{\tau }_{\mathrm{s}}}\mathrm{\Theta }(t-t_i^k)$$k$: neuron index, $i$: spike index
Spiking		Stochastic spike generation via inhomogeneous Poisson process with intensity $\varphi (u)=\rho e^{(u-u_{\text{th}})/\mathrm{\Delta }u}$; no reset after spike emission
E synapse model
Plasticity		Error-driven with continuous update (Equation 7, Equation 9)
F simulation parameters
Populations		$N=5$
Connectivity		$w_{\text{min}}=-20,w_{\text{max}}=20,w_{\text{shift}}\sim \{-15,15\},w_0=5$
Neuron model		$\rho =0.2\text{Hz},\mathrm{\Delta }u=1.0\text{mV},E_{\text{L}}=-70\text{mV},u_{\text{th}}=-55\text{mV},{\tau }_{\text{m}}=10\mathrm{ms},C_{\text{m}}=250\text{pF},{\tau }_{\text{s}}=2\mathrm{ms}$
Synapse model		$\eta =1.7,d=1\mathrm{ms},{\tau }_{\text{I}}=100.0\mathrm{ms}$
Input		$r_{\text{min}}=150\text{Hz},r_{\text{max}}=850\text{Hz},T=10,000\mathrm{ms},n_{\text{exp}}=15$
Other		$h=0.01\mathrm{ms},\delta t=5\mathrm{ms}$
G CGP parameters
Population		$\mu =4,p_{\text{mutation}}=0.045$
Genome		$n_{\text{inputs}}=3,n_{\text{outputs}}=1,n_{\text{rows}}=1,n_{\text{columns}}=12,l_{\text{max}}=12$
Primitives		Add, Sub, Mul, Div, Const(1.0)
EA		$\lambda =4,n_{\text{breeding}}=4,n_{\text{tournament}}=1$
Other		${\displaystyle \mathrm{m}\mathrm{a}\mathrm{x}\mathrm{g}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}=1000,\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{f}\mathrm{i}\mathrm{t}\mathrm{n}\mathrm{e}\mathrm{s}\mathrm{s}=0.0}$