OsloMet researcher spanning neural cellular automata, artificial life, and neuromorphic spiking networks
3
papers here
3
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-15) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Output metrics are not yet available in this snapshot. This axis would measure total publication volume, authorship roles, and year-over-year consistency of research production; no score is estimated here.
Impact—
Citation-based impact data is insufficient for this profile. This axis would measure citations, citation velocity, and influence on subsequent work; it is left unscored pending adequate bibliographic coverage.
Focus0/100
The focus score is 0 because the reviewed work splits evenly across three fields — machine learning, artificial life / neural cellular automata, and spiking neural networks / neuromorphic machine learning — each holding 0.33 of the field distribution. This is a breadth signal rather than a quality judgment: one methodological toolkit around NCA and self-organizing systems is carried across several communities instead of being concentrated in a single subfield or venue.
Collaboration39/100
The collaboration score is 39, based on 6 distinct co-authors in the snapshot. The pattern is small-team science: Nichele appears as last author on two of the three reviewed 2026 papers and as a middle author on the third, with no corresponding-author record. Recurring collaborators include Etienne Guichard, Sanyam Jain, Felix Simon Reimers, Andreas Massey, and Aliaksandr Hubin, suggesting stable, compact working groups.
Activity—
Temporal activity data is insufficient in this snapshot. This axis would measure publishing cadence, recency of contributions, and throughput over recent years; although all three reviewed papers are dated 2026, the sample is too small to produce a score.
Reproducibility73/100
Reproducibility readiness averages 73 across the three Evolve-reviewed papers. Two papers — the self-replicating NCA diversity study and the 'napping' paradigm for recurrent spiking networks — are rated high, while the NCA hidden-channel few-shot transfer paper is rated low, with Evolve noting thin statistical and baseline-fairness reporting. Overall the work reads as methodologically transparent, with empirical evaluation often limited to small-scale or single-dataset settings.
Research summary
Stefano Nichele is a researcher at OsloMet – Oslo Metropolitan University whose Evolve-reviewed work spans three evenly weighted areas: machine learning; artificial life with neural cellular automata (NCA); and neuromorphic spiking neural networks. Across three 2026 arXiv papers he appears as last author twice and middle author once, a pattern consistent with a senior role in small teams; the snapshot records six distinct co-authors and a collaboration score of 39. His NCA line includes a self-replicating substrate study quantifying a mutation-rate-driven phenotype–genotype diversity trade-off — rated high in reproducibility readiness, though demonstrated only at small scale — and a paper arguing that NCA hidden channels learn general features enabling few-shot transfer via injection, rated low because statistical and baseline-fairness reporting is thin and evidence is MNIST-scale. On the neuromorphic side, he co-authored the 'napping' paradigm for recurrent spiking networks: gradual noisy weight scaling inspired by the synaptic homeostasis hypothesis, which matches weight-normalization baselines on MNIST with a slight clustering gain. Across the three assessed papers, reproducibility readiness averages 73, with two rated high and one low. The even three-way field split yields a focus score of 0, signalling breadth across research communities rather than concentration in one.
Collaboration pattern
Collaboration in the snapshot is small-team: 6 distinct co-authors and a collaboration score of 39. Nichele occupies senior positions — last author on two of the three reviewed 2026 papers and middle author on the third — with no corresponding-author record. Recurring collaborators include Etienne Guichard, Sanyam Jain, Felix Simon Reimers, Andreas Massey, and Aliaksandr Hubin, consistent with compact, possibly mentorship-oriented groups spanning the NCA and neuromorphic lines.
Research keywords
Highlights
Self-replicating neural cellular automata reveal a mutation-rate-driven diversity trade-off
Hidden channels of neural cellular automata may carry transferable features
A 'napping' paradigm brings sleep-inspired regularization to recurrent spiking networks
Timeline
- 2026Last-author paper proposes that NCA hidden channels learn general features enabling few-shot transfer via injection
- 2026Last-author study quantifies emergent phenotypic and genotypic diversity in a self-replicating, open-ended NCA substrate
- 2026Co-authors, in a middle position, the 'napping' paradigm for recurrent spiking neural networks
- 2026All three reviewed 2026 papers receive Evolve reproducibility assessments averaging 73 (two high, one low)
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometric scores are derived from OpenAlex-based bibliometric data only; the output, impact, and activity axes lack sufficient underlying data in this snapshot.
- Coverage is limited to three 2026 arXiv preprints reviewed by Evolve; earlier work and peer-reviewed venue publications are not represented.
- The focus score of 0 reflects an even three-way field split and is a breadth signal, not a quality judgment.
- Identity is anchored to ORCID 0000-0003-4696-9872 and name-resolved from public bibliographic data; the frequent-coauthor list may contain name-resolution artifacts.