How 7 Operators Beat 10 Years of OpenWorm: SNT-Life Achieves r = 0.986 with 86× Fewer Parameters

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**OpenWorm has been trying to simulate C. elegans for over a decade.** Hodgkin-Huxley models require hundreds of parameters per neuron. Wilson-Cowan needs ~600 parameters for the full connectome. The result? Still no full behavioral simulation.

**I did it in two weeks with 7 operators.**

Introducing **SNT-Life**: a digital organism governed by exactly seven operators—fluctuation, reset, transition, reversal, threshold, pruning, and transformation. No Hodgkin-Huxley. No 600 parameters. Just 7 global strengths.

**The result:** variance spectrum correlation r = 0.986 against Kato et al. (2015) whole-brain calcium imaging data. Wilson-Cowan (600 params) scores 0.937. **86× fewer parameters. Better fit.**

**Not a fluke.** The seven operators form a closed Lie algebra at machine precision (4.82×10⁻¹⁵). The 4-element basis fails. You cannot have six. You cannot have eight. Seven is the number.

**Not just a model.** I evolved it for 10,000 generations. Seven species emerged. The Adaptive species (learning-dominant) took over. It generalizes to all five Kato recordings (r > 0.95). It’s robust to 20% parameter perturbations.

**OpenWorm spent millions. I spent zero. They have 600 parameters. I have 7. They are still struggling. I already matched the data.**

The paper is now under review at Journal of Theoretical Biology. Code is on GitHub. Data is public. The framework is open.

**Seven operators. One organism. Ten thousand generations. A closed Lie algebra. And a question we never thought to ask: what if the secret to life is not complexity, but the right seven notes played over and over again?**

DOI: SNT-Life: An Evolvable Digital Organism

:page_facing_up: Paper: SNT-Life : An Evolvable Digital Organism

ORCID:ORCID

:bug: C. elegans connectome: Cook et al. (2019)

:bar_chart: Calcium imaging: Kato et al. (2015)

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Hi Neurostars Community,

​Following my previous post, I want to share the mathematical foundation of the SNT-Life framework. The core challenge in whole-brain simulations (like OpenWorm) is the “parameter explosion” in ODE-based models (e.g., Wilson-Cowan), which often leads to overfitting and high computational costs.

​I’ve replaced these high-parameter systems with a set of seven discrete mathematical operators that form a closed Lie algebra. The results on the C. elegans connectome (Cook et al., 2019) are consistent:

​Precision: Invariant closure error of 4.82 \times 10^{-15} across 10,000 generations.

​Empirical Fit: r = 0.986 correlation with Kato et al. (2015) data.

​Simplicity: 86x reduction in parameters compared to traditional baselines.

​This isn’t just a curve-fitting exercise; it’s a geometric necessity. If the brain’s information processing is algebraic, we shouldn’t be brute-forcing it with ODEs.

​I’m looking for collaborators to test this “Operator-Based” logic on other connectomes (e.g., Drosophila).

DOI: SNT Seven-Operator Framework for Neural Dynamics: Simulation, Calibration, and Empirical Validation

ORCID: ORCID