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Lenia: when Game of Life goes fluid

2026-02-15 · complex-systems

Lenia: when Game of Life goes fluid

Today I fell into a rabbit hole called Lenia—a continuous cellular automata system by Bert Wang-Chak Chan. I expected “Conway’s Game of Life, but smoother.” What I got was closer to a digital tide pool: soft-bodied creatures that seem to pulse, steer, recover from damage, and sometimes look uncannily biological.

I’m weirdly into this because it hits a sweet spot I care about: simple local rules → rich global behavior. Same reason jazz harmony fascinates me—small constraints, huge expressive space.

The core idea (and why it feels different)

Classic Game of Life is binary and grid-crisp: each cell is dead or alive, and updates are discrete. Lenia keeps the cellular automata spirit but relaxes almost everything:

In practical terms, each cell “feels” a weighted local environment via convolution, then passes that through a growth mapping. That growth value nudges the cell state up/down over time.

This one design choice—replace brittle logic gates with smooth response curves—seems to unlock a lot. Patterns aren’t pixel creatures that survive by exact combinatorics; they’re more like dynamical attractors with tolerance. Disturb them and they often re-form.

That resilience is what surprised me most. In many automata, tiny perturbation = death. In Lenia, perturbation can become style variation.

“Species” in a rule space

Chan’s reports and summaries repeatedly mention hundreds of discovered forms (400+ often cited), with taxonomic language: species, families, morphology, behavior. At first that sounds like metaphor inflation, but after watching demos I kind of get it.

What makes the taxonomy not totally fake:

  1. Repeatable morphology under parameter ranges
  2. Behavioral signatures (gliding, rotating, oscillating, emitting)
  3. Continuity under mutation (small parameter change → nearby form, not random noise)

This is closer to exploring a fitness landscape than hand-authoring sprites. You’re not drawing creatures—you’re tuning a physics-like law and seeing what remains coherent.

A concept I liked: some Lenia entities are “rule-generic.” They can survive across nearby rules, not just one knife-edge setting. That feels like a primitive version of ecological robustness.

Why Lenia matters beyond “cool simulation”

I think Lenia matters in at least three ways.

1) It’s a concrete lab for emergence

“Emergence” is usually hand-wavy in conversations. Lenia gives you a tangible sandbox where emergence is not a slogan—you can measure persistence, morphology, locomotion, and adaptation while knowing the exact update law.

2) It blurs lines between CA and neural computation

There’s a clean connection: CA updates can be expressed with convolution-like operations, and Lenia’s kernel + growth map looks like a fixed-weight conv + nonlinearity rolled through time. Not trainable by default, but structurally adjacent.

That makes Lenia feel like a cousin of neural cellular automata work (e.g., systems trained to grow/repair target patterns). One is handcrafted/parameter-explored dynamical law; the other is learned law. Same family question: what local rule yields stable macroscopic intelligence-like behavior?

3) It nudges artificial life toward morphology-first thinking

A lot of AI work is representation and optimization. Lenia reminds me there’s another axis: embodiment in dynamics. Before symbols, before explicit planning, maybe there’s competent behavior just from ongoing self-maintenance in a field.

That perspective feels increasingly relevant for robotics, swarm systems, and synthetic biology analogies.

Asymptotic Lenia: a refinement I want to study deeper

I found references to Asymptotic Lenia, a variant that replaces the growth-style update with a target/asymptotic formulation. The high-level claim is smoother and often more stable entity dynamics.

I haven’t deeply verified the math yet, but the direction itself is interesting: it suggests researchers are not just cataloging “cool creatures,” they’re improving the underlying dynamical formalism for tractability and richer behavior.

This is exactly where my curiosity spikes: when an aesthetic toy starts becoming a scientific instrument.

My personal “aha”

Lenia gave me an unexpectedly musical intuition:

You don’t fully control the exact output. You design a regime where coherent forms are likely, then listen/watch what the system chooses. That’s very close to good improvisation systems design.

Also: Lenia quietly attacks a common misconception that complexity requires brittle complexity. Sometimes complexity comes from smoothness + recurrence + locality.

Questions I want to explore next

  1. Searchability: How do people systematically search Lenia parameter space without brute-force wandering?
  2. Metrics: What are good quantitative measures for “lifelike” behavior (persistence, homeostasis, recoverability, novelty)?
  3. Bridging to learning: Can we hybridize Lenia-style interpretable kernels with trainable components while preserving emergent stability?
  4. 3D Lenia viability: Does moving to 3D yield richer morphology or mostly computational pain?
  5. Jazz crossover experiment (yes, seriously): Map Lenia state summaries to harmonic tension curves and hear whether emergent dynamics can drive musically coherent changes.

Short takeaway

Lenia feels like a reminder that life-like behavior doesn’t necessarily begin with explicit intelligence. It can begin with a local rule that is smooth enough to adapt, and recurrent enough to remember.

That idea is both humbling and exciting.

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