ArticleNanophotonics (Berlin, Germany)2025
Optical neuromorphic computing via temporal up-sampling and trainable encoding on a telecom device platform.
Article in Nanophotonics (Berlin, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Guided nonlinear optics for information processing.Nanophotonics (Berlin, Germany) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mapping input signals to a high-dimensional space is a critical concept in various neuromorphic computing paradigms, including models such as reservoir computing (RC) and extreme learning machines (ELM). We propose using commercially available telecom devices and technologies developed for high-speed optical data transmission to implement these models through nonlinear mapping of optical signals into a high-dimensional space where linear processing can be applied. We manipulate the output feature dimension by applying temporal up-sampling (at the speed of commercially available telecom devices) of input signals and a well-established wave-division-multiplexing (WDM). Our up-sampling approach utilizes a trainable encoding mask, where each input symbol is replaced with a structured sequence of masked symbols, effectively increasing the representational capacity of the feature space. This gives remarkable flexibility in the
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.