Modeling C. elegans Neural Systems via the Universal Model Framework (UMF)

We explore how the Universal Model Framework (UMF) can enrich our understanding of the 302-
neuron connectome in Caenorhabditis elegans. Through fractal architectures, symmetry-regularized
learning, and prime-modulated dynamics, we propose that neural information processing may be governed by self-similar, prime-based constraints. Using fractal dimension analysis, symmetry constraints,
and quantum spin transitions, we illustrate how core features of the worm’s nervous system—especially
locomotion and sensory responses—could be captured by emergent fractal principles. We highlight how
an FPGA-based neural emulator (SI-Elegans) and conductance simulations (c302) can be integrated
with UMF-inspired fractal and prime-dynamic models, potentially leading to new ways of understanding—and engineering—biological intelligence.

• This project was developed by Marco Gericke, with structured assistance from a large language model. All scientific concepts and conclusions were generated, verified, and interpreted by the author.
• Dedicated to Peter Plichta, who envisioned the code before it could be computed.