Table 2. Model description after [49].

Model summary
Populations	254 populations: 32 areas (Table 1) with eight populations each (area TH: six)
Topology	—
Connectivity	area- and population-specific but otherwise random
Neuron model	leaky integrate-and-fire (LIF), fixed absolute refractory period (voltage clamp)
Synapse model	exponential postsynaptic currents
Plasticity	—
Input	independent homogeneous Poisson spike trains
Measurements	spiking activity
Populations
Type	Cortex
Elements	LIF neurons
Number of populations	32 areas with eight populations each (area TH: six), two per layer
Population size	N (area- and population-specific)
Connectivity
Type	source and target neurons drawn randomly with replacement (allowing autapses and multapses) with area- and population-specific connection probabilities
Weights	fixed, drawn from normal distribution with mean J such that postsynaptic potentials have a mean amplitude of 0.15 mV and standard deviation δJ = 0.1J; 4E to 2/3E increased by factor 2 [36]; weights of inhibitory connections increased by factor g; excitatory weights <0 and inhibitory weights >0 are redrawn; cortico-cortical weights onto excitatory and inhibitory populations increased by factor χ and ${\chi }_{\mathcal{I}}\chi$, respectively
Delays	fixed, drawn from Gaussian distribution with mean d and standard deviation δd = 0.5d; delays of inhibitory connections factor 2 smaller; delays rounded to the nearest multiple of the simulation step size h = 0.1 ms, inter-area delays drawn from a Gaussian distribution with mean d = s/vt, with distance s and transmission speed vt = 3.5 m/s [46]; and standard deviation δd = d/2, distances determined as the median of the distances between all vertex pairs of the two areas in their surface representation in F99 space, a standard macaque cortical surface included with Caret [47], where the vertex-to-vertex distance is the length of the shortest possible path without crossing the cortical surface [48] (see [35]), delays < 0.1 ms before rounding are redrawn
Neuron and synapse model
Name	LIF neuron
Type	leaky integrate-and-fire, exponential synaptic current inputs
Subthreshold dynamics	$\frac{\mathrm{d}V}{\mathrm{d}t}=-\frac{V-E_{\mathrm{L}}}{{\tau }_{\mathrm{m}}}+\frac{I_{\mathrm{s}}\left(t\right)}{C_{\mathrm{m}}}$ if (t > t* + τr) V(t) = Vr else $I_{\mathrm{s}}\left(t\right)={\sum }_{i,k}{J}_k{e}^{-(t-t_i^k)/{\tau }_{\mathrm{s}}}\Theta (t-t_i^k)$ k: neuron index, i: spike index
Spiking	If V(t−) < θ ∧ V(t+) ≥ θ 1. set t* = t, 2. emit spike with time stamp t*
Input
Type	Background
Target	LIF neurons
Description	independent Poisson spikes (for each neuron, fixed rate νext = νbgkext with average external spike rate νbg = 10 spikes/s and number of external inputs per population kext, weight J)
Measurements
Spiking activity