Evidence map›Paper›PMID 42187374›Full record

ArticleBiomimetics (Basel, Switzerland)2026

A Dynamic Virtual Channel Approach to Enhance Retinal Prosthetic Precision.

Zhengyang Liu, Tianruo Guo, Yuyan He, Shiwei Zheng, Xiaoyu Song, Cuixia Dai, Jiaxi Li, Xinyu Chai, Yao Chen, Liming Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Zhengyang LiuSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0009-0007-6561-1813
Tianruo GuoGraduate School of Biomedical Engineering, The University of New South Wales, Sydney, NSW 2052, Australia.
Yuyan HeSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Shiwei ZhengSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Xiaoyu SongSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0000-0002-6766-6945
Cuixia DaiCollege of Sciences, Shanghai Institute of Technology, Shanghai 201418, China.
Jiaxi LiSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Xinyu ChaiSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Yao ChenSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0000-0003-1975-4868
Liming LiSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Funding

National Health and Medical Research Council RG251136National Natural Science Foundation of China 61971280National Natural Science Foundation of China 62073221National Natural Science Foundation of China 62175156National Natural Science Foundation of China 62176151National Natural Science Foundation of China 62371294Retina Australia Research Grants RG253022Shanghai Jiao Tong University Medical Engineering Cross Research YG2024QNA54The National Key R&D Program of China 2025YFF0518002University of New South Wales (UNSW) 3Rs Fund RG245361
6 · The paper itself

Abstract

Visual prostheses aim to approximate biomimetic visual function by electrically simulating surviving retinal neurons. Improving the spatial resolution of electrically elicited artificial vision remains a critical challenge for retinal prostheses. We investigate how dynamic virtual channel (DVC) parameters shape retinal ganglion cell (RGC) population responses to improve spatial precision and activation efficiency in epiretinal stimulation. We developed a computational modeling framework to quantify DVC performance using a hierarchical optimization strategy. First, static virtual channels (SVCs) were used to map how current ratio (α) and stimulus intensity govern RGC activation, defining an optimal SVC parameter space. Building on this baseline, DVC protocols were refined by evaluating the combined effects of inter-virtual-channel interval (ΔT), α, and intensity. This strategy significantly reduces the complexity of DVC parameter optimization. Under SVC stimulation, increasing intensity improved the linearity of receptive field (RF) centroid displacement with α, while α and intensity jointly set RF centroid location and activated area. Under DVC stimulation, ΔT strongly modulated RGC activation, especially at short intervals. Initializing from SVC-optimized parameters, tuning ΔT and intensity produced more confined activation at lower stimulus intensities than SVC, indicating that DVC can serve as a novel stimulation strategy to enhance spatial precision and activation efficiency in retinal stimulation. This study provides the first systematic analysis of retinal DVC stimulation and a practical optimization framework for next-generation prostheses.

Indexed as

artificial visioncomputational modelingdynamic virtual channelepiretinal prosthesis

Identifiers

PMID42187374
PMCPMC13204981

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.