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Stefano Nichele

Oslo Metropolitan University (OsloMet)

Builder The claim is about what the hardware does, and the philosophy is left open

Stefano Nichele is a professor at Oslo Metropolitan University whose work sits between complex systems and machine learning, with a long line of papers using biological neural structures as computational substrates. The recurring method is to take real anatomical data, worm connectomes, human structural brain networks, and run them through the reservoir-computing framework, where the biological graph serves as an untrained recurrent reservoir and only the readout learns.

The September 2026 arXiv preprint “Benchmarking the Connectomes of Caenorhabditis elegans within the Reservoir Computing Framework” (arXiv:2609.30508, with Felix S. Reimers, Ola Huse Ramstad, and Aliaksandr Hubin) is the sharpest statement of his program’s central finding. Connectomes derived at different worm ages, under three different ways of measuring intercellular connections, were run as echo state networks on neuro-inspired tasks against equal-size randomized null models. The biological wiring and bio-informed input-output placement did not reliably win. Randomized reservoirs often outperformed the real connectomes.

That result matters for brain emulation and for any claim that structure alone carries function. If a wiring diagram’s computational value depends heavily on reservoir configuration and derivation method, then the connectome-to-function step needs explicit dynamics and validation, which is exactly the boundary the emulation field works against. The companion framework “Connectome-to-Function” (arXiv:2609.06093) supplies the generative latent machinery for that step, learning a structural space in which connectome graphs and their reservoir-computing behavior can be analyzed together.

Known for. Reservoir computing on biological connectomes, C. elegans nervous-system emulation, morphogenesis in cellular automata

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