Appendix 1—table 3. : Description of the network model used in the correlation-driven learning task (4.7).

A model summary
Populations		2
Topology		—
Connectivity		Feedforward with fixed connection probability
Neuron model		Leaky integrate-and-fire (LIF) with exponential post-synaptic currents
Plasticity		Reward-driven
Measurements		Spikes
B populations
Name	Elements	Size
Input	Spike generators with pre-defined spike trains (see 4.5)	$N$
Output	LIF neuron	1
C connectivity
Source	Target	Pattern
Input	Output	Fixed pairwise connection probability $p$; synaptic delay $d$; random initial weights from $𝒩(0,{\sigma }_w^2)$
D neuron model
Type		LIF neuron with exponential post-synaptic currents
Subthreshold dynamics		$\frac{\mathrm{d}u(t)}{\mathrm{d}t}=-\frac{u(t)-E_{\mathrm{L}}}{{\tau }_{\mathrm{m}}}+\frac{I_{\mathrm{s}}(t)}{C_{\mathrm{m}}}$ if not refractory
$u(t)=u_{\mathrm{r}}$ else $I_{\mathrm{s}}(t)={\sum }_{i,k}{w}_k{e}^{-(t-t_i^k)/{\tau }_{\mathrm{s}}}\mathrm{\Theta }(t-t_i^k)$, $k$: neuron index, $i$: spike index
Spiking		Stochastic spike generation via inhomogeneous Poisson process with intensity $\varphi (u)=\rho e^{(u-u_{\text{th}})/\mathrm{\Delta }u}$; reset of $u$ to $u_{\text{r}}$ after spike emission and refractory period of ${\tau }_{\text{r}}$
E synapse model
Plasticity		Reward-driven with episodic update (Equation 2, Equation 3)
Other		Each synapse stores an eligibility trace (Equation 22)
F simulation parameters
Populations		$N=50$
Connectivity		$p=0.8,{\sigma }_w={10}^3\text{pA}$
Neuron model		${\displaystyle \begin{array}{l}\rho =0.01\text{Hz},\mathrm{\Delta }u=0.2\text{mV},E_{\text{L}}=-70\text{mV},u_{\text{r}}=-70\text{mV},u_{\text{th}}=-55\text{mV},\\ {\tau }_{\text{m}}=10\mathrm{m}\mathrm{s},C_{\text{m}}=250\text{pF},{\tau }_{\text{r}}=2\mathrm{m}\mathrm{s},{\tau }_{\text{s}}=2\mathrm{m}\mathrm{s}\end{array}}$
Synapse model		${\displaystyle \eta =10,{\tau }_{\text{M}}=500\mathrm{m}\mathrm{s},d=1\mathrm{m}\mathrm{s}}$
Input		$M=30,r=6\text{Hz},T=500\mathrm{ms},n_{\text{training}}=500,n_{\text{exp}}=10$
Other		$h=0.01\mathrm{ms},R\in \{-1,1\},m_{\text{r}}=100$
G CGP parameters
Population		$\mu =8,p_{\text{mutation}}=0.05$
Genome		$n_{\text{inputs}}=2,n_{\text{outputs}}=1,n_{\text{rows}}=1,n_{\text{columns}}=5,l_{\text{max}}=5$
Primitives		Add, Sub, Mul, Div, Pow, Const(1.0)
EA		$\lambda =8,n_{\text{breeding}}=8,n_{\text{tournament}}=1$
Other		${\displaystyle \mathrm{m}\mathrm{a}\mathrm{x}\mathrm{g}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}=2000,\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{f}\mathrm{i}\mathrm{t}\mathrm{n}\mathrm{e}\mathrm{s}\mathrm{s}=10.0}$