Same model as the 3D version, drawn as a flat node-link diagram instead of an orbiting sphere: each dot is a model neuron, dots are grouped into modules (rings), each wired internally in a cycle. Integration controls how many extra links exist between modules — at 0% the modules are functionally isolated islands, at 100% they form one densely cross-wired mesh.
Every frame, activation decays, picks up random spontaneous firing scaled by noise, then diffuses along every active link toward its neighbour's level. Ignite forces one random neuron to full activation so you can watch how far the resulting cascade actually travels.
Φ ≈ synchrony × integration × mean(activation)
synchrony = 1 / (1 + Var(module means) × k)
Inspired by Tononi's Integrated Information Theory:
Φ_IIT = EI(whole system) − Σ EI(parts taken separately)
(a system is "more conscious" the more its whole exceeds the sum of its parts)
- Integration — density of inter-module wiring; low integration keeps information local to each module (a candidate "philosophical zombie" — active but not unified), high integration lets a single ignition become a global, workspace-wide broadcast.
- Spontaneous noise — background random firing rate; too low and the network stays dark, too high and every module free-runs independently regardless of wiring.
- Modules — how many specialized clusters the network is split into, redrawn around a fixed layout so the whole network always stays in view.
- Ignite — ignites one random neuron at full strength, the cleanest way to see whether a signal stays local or reaches the whole network.
The Φ readout here is a deliberately simplified, illustrative proxy — not the real (NP-hard) IIT computation — built from how synchronized the modules are, how richly they're wired together, and how much activity is actually happening.