Slime Mould

2. Code and algorithm implementations

Transport-network solvers implement a graph, sources and sinks, a flow calculation, and an adaptation update. They are appropriate when the research question concerns routing, cost, redundancy, or convergence—not cellular appearance. The public Physarum Polycephalum Inspired Algorithm repository explicitly labels its original-PPA reproduction and related optimisation variants; read its licence and provenance before reuse.

Multi-agent trail models are a different family. Agents sample a field ahead, steer toward stronger sensed values, deposit trail, and the field diffuses and decays. Jeff Jones’s work distinguishes particle approximations and environmental influences; a simulation’s sensor angle, turn angle, deposit, diffusion, decay, attractants, repellents, and boundary rule are its actual model. [4] A small Python example, ammitra/Physarum, identifies itself as an inspired model and gives parameters; it is a learning reference, not an authority on biology.

Reaction–diffusion is another distinct route to branching and labyrinthine images. It evolves coupled fields rather than agents or adaptive tube conductivities. Similar visual output never establishes shared mechanism.

3. Visual, artist, and coder interpretations

For visual work, the productive translation is usually structural: terminals become attractors, reinforced links become line weight, weakened links become erased or lighter paths, and changing conditions produce rerouting. A plotter interpretation may start from a graph solver, a multi-agent trail field, a reaction–diffusion field, or a hand-authored graphic rule. These should be credited by their actual method.

Named technical/creative examples include CreativeCodingLab’s Polyphorm, which documents its Monte Carlo Physarum Machine as inspired by foraging behaviour and uses it for cosmic-web data visualisation, and Barbelot’s Physarum3D, a Unity implementation citing Jeff Jones’s transport-network model. They are computational interpretations, not biological images or claims of literal organism simulation. No third-party artwork is embedded on this page.

Reading the diagram

The terminals stand for resources or required connections. Thick routes represent edges strengthened by a selected update rule; thin and grey routes remain alternatives. Retaining alternatives can model resilience; pruning can reduce total route length. This is a graph diagram only, not a model organism image.

Algorithmics interpretation

Why it matters here. The reference supports multiple growth-pattern interpretations in plotter and data-math work: observed network behaviour, transport optimisation, multi-agent trails, reaction–diffusion analogies, and graphic path translations. The important distinction is made before drawing: choose the model, expose its parameters, and name the limits of the claim.

Annotated sources

1. PNAS, “Mechanism of signal propagation in Physarum polycephalum” — experimental and mathematical work on signal propagation.
2. Nakagaki, Yamada and Tóth, “Path finding by tube morphogenesis in an amoeboid organism,” 2001 — maze experiment and adaptive-network model.
3. Bonifaci, Mehlhorn and Varma, “Physarum can compute shortest paths,” 2012 — analysis of a defined Physarum solver.
4. Jeff Jones, “Mechanisms Inducing Parallel Computation in a Model of Physarum polycephalum Transport Networks” — parameters and behaviour of a particle model.
5. CreativeCodingLab / Polyphorm — source repository and technical documentation for a Physarum-inspired visualisation tool.

All linked papers, code repositories, artworks, and organism imagery retain their authors’ and rightsholders’ rights. The only image embedded here is the original Algorithmics explanatory SVG above.