Computational neuroscientist linking neural dynamics and visual coding with manifold-based machine learning
2
papers here
1
reviewed by Evolve
1
first-author
0
corresponding
—
works · OpenAlex
3
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-09-20) 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—
Unscored: this snapshot lacks a verified total publication count and venue-complete work list, offering only per-year OpenAlex tallies. The output axis would measure aggregate publication volume, its venue mix, and the author's role distribution over time.
Impact—
Unscored: the snapshot reports only an h-index of 3 and an i10-index of 2, without full per-paper citation aggregates. This axis would measure citation uptake, influence within and beyond neuroscience, and the durability of the work's reception.
Focus100/100
The focus score of 100 reflects an exceptionally concentrated topic profile. OpenAlex places 22 of 30 topic tags in two adjacent areas — neural dynamics and brain function (14) and visual perception and processing mechanisms (8) — with smaller clusters in insect neurobiology (4), face and expression recognition (2), and neural network applications (2). The 2026 move into manifold-based clustering theory extends this vision-centered agenda rather than diluting it.
Collaboration39/100
Collaboration scores 39, consistent with a compact author network: only 6 distinct coauthors appear in the recent record. Recurring partners include Steven W. Zucker (listed under two name variants), G. D. Field, M. P. Stryker, and Savik Kinger, indicating small, deep teams rather than broad consortia. Recent catalog work spans a first-author neurophysiology preprint and a middle-author machine-learning paper, and one coauthor entry ("Dyballa, L.") is the researcher's own name variant, a known disambiguation artifact.
Activity75/100
Activity scores 75 on a clearly accelerating curve: OpenAlex records 19 works distributed as 1 (2016), 2 (2018), 1 (2022), 3 (2023), 5 (2024), and 7 (2026), with 12 works in the last three years — the densest stretch on record. Both 2026 catalog entries, a first-author bioRxiv preprint and an arXiv co-authorship, fall inside this surge, showing simultaneous output in neuroscience and machine-learning venues.
Reproducibility55/100
Reproducibility scores 55, but this rests on a single assessed paper (reproPapers: 1, mean 55): the 2026 arXiv clustering paper, rated medium readiness, where Evolve credited sound theory and honest evaluation while flagging that the theorem-to-algorithm link remains heuristic. One paper is too small a base for a stable estimate, so read this score as an early, provisional signal rather than a settled record.
Research summary
Luciano Dyballa (IE University, Spain; ORCID 0000-0003-1996-2173) works at the junction of computational neuroscience and machine learning. OpenAlex topic assignments concentrate 22 of 30 tags in neural dynamics and brain function (14) and visual perception and processing mechanisms (8), with secondary clusters in insect neurobiology, face and expression recognition, and neural network applications — the basis for a maximum focus score of 100. The record shows 19 indexed works from 2016 to 2026, accelerating to 12 works in the last three years (activity 75), including 7 in 2026 alone. Two 2026 catalog entries illustrate the range: a first-author bioRxiv preprint using encoding manifolds to characterize functional organization and natural-scene responses across mouse visual cortical areas, and a middle-author arXiv paper, "Bracketing Uncertainty in Clustering Under the Manifold Hypothesis", whose MBC method Evolve reviewers judged theoretically sound and honestly evaluated while flagging a heuristic theorem-to-algorithm link (medium reproducibility readiness). Collaboration is compact — 6 distinct coauthors including Steven W. Zucker, G. D. Field, M. P. Stryker, and Savik Kinger — for a collaboration score of 39. The citation footprint is early-stage (h-index 3, i10-index 2), reproducibility (55) rests on a single assessed paper, and the output and impact axes remain unscored.
Collaboration pattern
This is a small-network profile: 6 distinct coauthors and a collaboration score of 39. Steven W. Zucker recurs under two name variants, alongside G. D. Field, M. P. Stryker, and Savik Kinger, pointing to a stable core team. Recent output mixes first-author neurophysiology on bioRxiv with middle-author machine-learning theory on arXiv. Note that "Dyballa, L." in the coauthor list is the researcher's own name variant, an OpenAlex artifact.
Research keywords
Highlights
Bracketing the number of clusters under the manifold hypothesis
Encoding manifolds across mouse visual cortex
A 2026 bioRxiv preprint, first-authored, uses encoding manifolds to characterize functional organization and natural-scene responses across mouse visual cortical areas, extending the profile's two dominant topics — neural dynamics and visual perception — into population-level analysis. This paper is not yet reviewed by Evolve (no slug or verdict on record).
One geometric thread from cortex to clustering
The two 2026 catalog papers share a manifold-geometry theme: encoding manifolds for visual cortical populations on the neuroscience side, and clustering guarantees under the manifold hypothesis on the machine-learning side. OpenAlex's field distribution currently tags machine learning (statistical learning, manifold geometry, unsupervised clustering) as the sole field, underscoring how recent this bridge is.
Timeline
- 2016First indexed OpenAlex work appears, with 1 work recorded that year.
- 20182 indexed works recorded, the highest annual count to that point.
- 2022A quiet year with 1 indexed work, before the current ramp-up begins.
- 2023Output picks up: 3 indexed works recorded.
- 20245 indexed works, up from 3 the previous year.
- 20267 indexed works — the most productive year on record — including a first-author bioRxiv preprint on encoding manifolds in mouse visual cortex and a middle-author arXiv paper on manifold-based clustering uncertainty.
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.
- Bibliometrics are drawn from OpenAlex only (identity: openalex); non-indexed outputs and citation-indexing delays may distort totals.
- The reproducibility score (55) averages a single assessed paper and should be treated as provisional.
- Output and impact axes are unscored because full publication totals and per-paper citation aggregates are absent from this snapshot.
- Profile is name-resolved from public bibliographic data; affiliation (IE University, ES) and ORCID are as provided, and the coauthor list contains a self name-variant ("Dyballa, L.").