OSCR

Direct laser carbonization of parylene-C toward microelectrodes for <i>in vivo</i> action potential detection.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches
  1. [1] § Methodology › Data analysis ↔ src/electrophysiology/noise.py, lines 1–23 · score 0.68 · median absolute deviation, standard deviation, Noise, spikes
  2. [2] § Methodology › Data analysis ↔ src/electrophysiology/spikes/template_matching.py, lines 11–72 · score 0.63 · cluster memberships, iterative template, distances, GMM, waveform, spikes
  3. [3] § Methodology › Data analysis ↔ src/electrophysiology/spikes/cluster.py, lines 17–57 · score 0.62 · Gaussian Mixture, feature space, component, GMM, clustering, spike
  4. [4] § Methodology › Data analysis ↔ scripts/analyze_raman.py, lines 79–111 · score 0.60 · window length, Raman spectra, smooth, intensity, filter
  5. [5] § Results › Effect of electrode size on AP detection ↔ scripts/analyze_putative_neurons.py, lines 1–28 · score 0.59 · putative neurons, firing frequencies, electrophysiological recordings, Clustering, spikes

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Python · 35 lines · 1.3 KB · no license · 1 match

  1. """Robust noise estimation for electrophysiology signals.
  3. Estimates the background noise level as a Gaussian-consistent standard
  4. deviation derived from the median absolute deviation (MAD), which is robust
  5. to the heavy-tailed contamination introduced by spikes:
  7. sigma_n = median(|x - median(x)|) / 0.6745
  9. This is the robust noise estimator widely used for extracellular spike
  10. detection. See:
  12. Quiroga, R. Q., Nadasdy, Z., & Ben-Shaul, Y. (2004). Unsupervised spike
  13. detection and sorting with wavelets and superparamagnetic clustering.
  14. Neural Computation, 16(8), 1661-1687.
  15. https://doi.org/10.1162/089976604774201631
  17. The scaling constant 0.6745 is the 0.75 quantile of the standard normal
  18. distribution (Phi^-1(0.75)); dividing the MAD by it makes the estimate
  19. consistent with the standard deviation for normally distributed noise
  20. (equivalently MAD * 1.4826).
  21. """
  23. import numpy as np
  25. # Phi^-1(0.75): scales the MAD to a Gaussian-consistent standard deviation.
  26. MAD_SCALE = 0.6744897501960817
  28. def calculate_noise(signal: np.ndarray) -> float:
  29. signal = np.asarray(signal, dtype=float)
  30. if signal.size == 0:
  31. raise RuntimeError("calculate_noise(): signal is empty")
  33. median = np.median(signal)
  34. mad = np.median(np.abs(signal - median)) / MAD_SCALE
  35. return float(mad)

noise.py at commit 6ed2536, no license · at the source

Overview

Authors: Virgil Christian G Castillo1, Yasumi Ohta1, Yoshinori Sunaga1, Kuang-Chih Tso1, Jun Ohta1
  1. Strategic Initiative for Research and Innovation, Nara Institute of Science and Technology, Nara, Japan
Institutions: Nara Institute of Science and Technology (Japan)
Journal: Frontiers in neuroscience, volume 20, article 1893619
Dates: received 28 May 2026; accepted 4 August 2026; published online 25 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnins.2026.1893619 · PMID 42712433 · PMCID PMC13550115 · OpenAlex W7204172468
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Spectral & time-frequency, Preprocessing, Smoothing, state filtering, decompositions, Machine learning, Single-unit activity, calcium imaging, Connectivity
Keywords: action potentials, carbon microelectrodes, electrophysiology, laser carbonization, neural interfaces, red nucleus
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Japan Society for the Promotion of Science (25K24569)
Citations: not cited yet (Europe PMC); 79 references in the paper

Abstract

Integrating recording electrodes into optical imaging devices presents significant fabrication and material challenges, particularly for lightweight platforms used in freely moving applications. Here, we report an optimized laser carbonization process for the direct fabrication of high-performance carbon microelectrodes compatible with on-chip imaging platforms. We systematically investigated the influence of laser power and repetition rate on the carbonization of parylene-C to maximize graphitization while avoiding ablation. Raman spectroscopy confirmed the formation of graphitic carbon, with the lowest average D-to-G band intensity ratio (ID/IG = 0.63) obtained at 303.86 μW. However, this laser condition resulted in excessive material loss from ablation. The optimal balance between carbon quality and material retention was achieved at 212.69 μW and 30 Hz. Using these parameters, we fabricated implantable carbon microelectrode probes of different sizes (100 × 100, 20 × 20, and 10 × 10 μm2) on flexible polyimide substrates. The impedances of these electrodes were 12.10 ± 0.22 kΩ, 596.38 ± 84.86 kΩ, and 8.969 ± 0.991 MΩ for 100 × 100, 20 × 20, and 10 × 10 μm2 laser carbonized electrodes, respectively. While larger electrodes offered lower impedance, spontaneous single-unit action potentials were only detectable using the 20 × 20 μm2 electrodes, which provided the necessary spatial selectivity to isolate individual neurons. These results establish a maskless fabrication process for high-quality, flexible carbon microelectrodes and demonstrate their suitability for electrophysiological recording.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

virgil-castillo/laser-carbonized-parylene-C-microelectrodes

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 6ed2536f5db37e8c0918b8ed745dfd8e19c9ee72, 9 September 2026
Languages: Python (46), Jupyter (1)
Size: 92 files, 47 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README, environment (environment.yml, pyproject.toml), tests, 1 notebook
Not found: license file, CITATION.cff, continuous integration, documentation
Tools: NumPy (29 files), Matplotlib (14 files), pandas (5 files), scikit-learn (4 files), Pillow (3 files), SciPy (2 files), scikit-image (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
48 files
At the source: github.com/virgil-castillo/laser-carbonized-parylene-C-microelectrodes

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 47 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/virgil-castillo/laser-carbonized-parylene-C-microelectrodes.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Funding: added Japan Society for the Promotion of Science: 25K24569

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 77 references.

Cite

This paper

Castillo, V. C. G., Ohta, Y., Sunaga, Y., Tso, K.-C., & Ohta, J. (2026). Direct laser carbonization of parylene-C toward microelectrodes for <i>in vivo</i> action potential detection. Frontiers in neuroscience, 20, 1893619. https://doi.org/10.3389/fnins.2026.1893619

BibTeX

@article{castillo2026direct,
author = {Castillo, Virgil Christian G and Ohta, Yasumi and Sunaga, Yoshinori and Tso, Kuang-Chih and Ohta, Jun},
title = {{Direct laser carbonization of parylene-C toward microelectrodes for \<i\>in vivo\</i\> action potential detection}},
journal = {Frontiers in neuroscience},
year = {2026},
month = aug,
volume = {20},
pages = {1893619},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/fnins.2026.1893619},
url = {https://doi.org/10.3389/fnins.2026.1893619},
pmid = {42712433},
pmcid = {PMC13550115}
}

RIS

TY - JOUR
AU - Castillo, Virgil Christian G
AU - Ohta, Yasumi
AU - Sunaga, Yoshinori
AU - Tso, Kuang-Chih
AU - Ohta, Jun
TI - Direct laser carbonization of parylene-C toward microelectrodes for <i>in vivo</i> action potential detection
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/08/25
VL - 20
SP - 1893619
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/fnins.2026.1893619
UR - https://doi.org/10.3389/fnins.2026.1893619
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fnins.2026.1893619",
"type": "article-journal",
"title": "Direct laser carbonization of parylene-C toward microelectrodes for <i>in vivo</i> action potential detection",
"container-title": "Frontiers in neuroscience",
"author": [
{
"family": "Castillo",
"given": "Virgil Christian G"
},
{
"family": "Ohta",
"given": "Yasumi"
},
{
"family": "Sunaga",
"given": "Yoshinori"
},
{
"family": "Tso",
"given": "Kuang-Chih"
},
{
"family": "Ohta",
"given": "Jun"
}
],
"container-title-short": "Front Neurosci",
"volume": "20",
"page": "1893619",
"DOI": "10.3389/fnins.2026.1893619",
"PMID": "42712433",
"PMCID": "PMC13550115",
"ISSN": "1662-4548",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fnins.2026.1893619",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
25
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1111/ejn.70582 [code]
Multifiber Array-Based Photometry System for Multiregional Functional Mapping in the Mouse Brain.
Journal: The European journal of neuroscience
In common: scikit-image, Pillow, pandas, 3 other tools, 5 references
[2] doi:10.1371/journal.pcbi.1013441 [code]
Large vision model framework for automated C. elegans analysis: From static morphometry to dynamic neural activity.
Journal: PLoS computational biology
In common: scikit-image, Pillow, pandas, 3 other tools, 3 references
[3] doi:10.1038/s41467-026-71887-x [code]
Magnetic resonance identification tags for ultra-flexible electrodes.
Journal: Nature communications
In common: scikit-image, scikit-learn, pandas, 3 other tools, 3 references
[4] doi:10.7554/elife.110588 [code]
Opening the black box toward a modular approach to spike sorting.
Journal: eLife
In common: scikit-image, Pillow, scikit-learn, 4 other tools, 2 references
[5] doi:10.1038/s41593-026-02232-0 [code]
Entorhinal cortex represents task-relevant remote locations independently of CA1.
Journal: Nature neuroscience
In common: scikit-image, Pillow, scikit-learn, 4 other tools, 2 references
[6] doi:10.7554/elife.110074 [code]
Disentangling cephalopod chromatophores motor units with computer vision.
Journal: eLife
In common: scikit-image, Pillow, scikit-learn, 4 other tools, 2 references
[7] doi:10.7554/elife.110277 [code]
Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals.
Journal: eLife
In common: scikit-learn, pandas, SciPy, 2 other tools, 3 references
[8] doi:10.1016/j.crmeth.2026.101421 [code]
EthoPy provides an accessible platform for reproducible behavioral neuroscience.
Journal: Cell reports methods
In common: scikit-learn, pandas, SciPy, 2 other tools, 4 references
[9] doi:10.1038/s41598-026-61605-4 [code]
Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations.
Journal: Scientific reports
In common: scikit-image, Pillow, scikit-learn, 3 other tools, 2 references
[10] doi:10.1371/journal.pcbi.1014571 [code]
SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.
Journal: PLoS computational biology
In common: scikit-image, Pillow, scikit-learn, 4 other tools, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.