Table 2.

Model description after Nordlie et al. (2009).

Model summary
Populations	34 areas (Table 1) with a total of 254 populations. The model consists of about 3.5 million neurons and 43 billion synapses.
Geometry	—
Connectivity	area- and population-specific but otherwise random
Neuron model	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	34 areas with 8 populations each (areas caudalanteriorcingulate, caudalmiddlefrontal, entorhinal, lateraloccipital, parsorbitalis, precentral, rostralanteriorcingulate have 6, and the parahippocampal area has 4), 2 per layer
Population size	(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. The total number of synapses between populations is fixed, corresponding to the “Random, fixed total number” rule described by Senk et al. (2022).
Weights	fixed, drawn from normal distribution with mean such that postsynaptic potentials have a mean amplitude of and standard deviation ; 4E to 2/3E increased by factor (cf. Potjans and Diesmann 2014); weights of inhibitory connections increased by factor ; excitatory weights and inhibitory weights are redrawn; inter-areal weights onto inhibitory populations increased by factor and onto excitatory and inhibitory populations increased by factor
Delays	fixed, drawn from truncated lognormal distribution with mean and standard deviation ; delays of inhibitory connections factor smaller; delays rounded to the nearest multiple of the simulation step size , inter-area delays drawn from a truncated lognormal distribution with mean , with distance and average transmission speed (Girard et al. 2001); and standard deviation , distances determined as the median of the distances between all vertex pairs of the 2 areas in the DTI data (Goulas et al. 2016), delays before rounding are redrawn
Neuron and synapse model
Name	LIF neuron
Type	LIF, exponential synaptic current inputs
Subthreshold dynamics	,   else, , : neuron index, : spike index, : Heaviside step function
Spiking	If 1. set , 2. emit spike with time stamp
Input
Type	Background
Target	LIF neurons
Description	Independent homogeneous Poisson spike trains to all neurons in the network; rate fixed such that the mean input, measured relative to rheobase, is
Measurements
Spiking activity