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Amphibian-inspired neuromorphic dynamic vision systems based on ferroelectric field-effect transistor.

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Paper

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The authors' code

Python · 70 lines · 1.9 KB · CC-BY-4.0

  1. import tensorflow as tf
  2. from keras.models import Model
  3. from keras.layers import (
  4. Input, Conv3D, BatchNormalization, Activation,
  5. Dense, Flatten
  6. )
  8. # ============================================
  9. def build_model(input_shape=(10, 240, 135, 1)):
  10. inputs = Input(shape=input_shape)
  12. x = Conv3D(32, (5, 5, 1), strides=(1, 5, 5), padding='same')(inputs)
  13. x = BatchNormalization()(x)
  14. x = Activation('relu')(x)
  16. x = Conv3D(64, (1, 1, 5), strides=(5, 1, 1), padding='same')(x)
  17. x = BatchNormalization()(x)
  18. x = Activation('relu')(x)
  20. x = Conv3D(64, (3, 3, 1), strides=(1, 3, 3), padding='same')(x)
  21. x = BatchNormalization()(x)
  22. x = Activation('relu')(x)
  24. x = Conv3D(64, (1, 1, 3), strides=(2, 1, 1), padding='same')(x)
  25. x = BatchNormalization()(x)
  26. x = Activation('relu')(x)
  28. # x = Lambda(lambda x: tf.reduce_mean(x, axis=1))(x)
  29. # x = Conv2D(32, 3, padding='same')(x)
  31. x = Flatten()(x)
  33. x = Dense(1024, activation='relu')(x)
  35. x = Dense(128, activation='relu')(x)
  37. x = Dense(5, activation='softmax')(x)
  39. model = Model(inputs, x, name="build_model")
  41. return model
  43. # ============================================
  44. def compile_model(model, lr=1e-5):
  45. optimizer = tf.keras.optimizers.Adam(learning_rate=lr, clipnorm=1.0)
  47. losses = tf.keras.losses.categorical_crossentropy
  49. model.compile(
  50. optimizer=optimizer,
  51. loss=losses,
  52. metrics='accuracy'
  53. )
  55. return model
  57. # ============================================
  59. if __name__ == "__main__":
  60. model = build_model()
  62. model = compile_model(model, lr=1e-3)
  64. model.summary()
  66. # lr_scheduler = tf.keras.optimizers.schedules.CosineDecay(
  67. # initial_learning_rate=1e-4,
  68. # decay_steps=100 * steps_per_epoch,
  69. # alpha=1e-6
  70. # )

face_model.py, under CC-BY-4.0 · at the source

Overview

Authors: Yongbiao Zhai1, Peijie Chen1, Ying Luo1, Ziyu Lv1, Guanglong Ding1, Junjie Yang1, Minglin Zheng1, Ye Zhou2, Yang Chai3,4, Su-Ting Han5,6
ORCID iDs: Ziyu Lv, Guanglong Ding, Ye Zhou, Yang Chai, Su-Ting Han
  1. College of Electronics and Information Engineering, Shenzhen University,Shenzhen, P. R. China
  2. Institute for Advanced Study, Shenzhen University,Shenzhen, P. R. China
  3. Department of Applied Physics, The Hong Kong Polytechnic University,Hong Kong, China
  4. Joint Research Center of Microelectronics, The Hong Kong Polytechnic University,Hong Kong, China
  5. Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy, The Hong Kong Polytechnic University, Hung Hom, Kowloon,Hong Kong, P. R. China
  6. The Hong Kong Polytechnic University Shenzhen Research Institute,Shenzhen, PR China
Institutions: Shenzhen University (China); Hong Kong Polytechnic University (Hong Kong SAR China)
Journal: Nature communications, volume 17, issue 1, article 8641
Dates: received 3 September 2025; accepted 10 June 2026; published online 23 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74769-4 · PMID 42336895 · PMCID PMC13486663 · OpenAlex W7165630533
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: cognitive (subfield)
Keywords: Electronic devices
MeSH: Amphibians*, Transistors, Electronic*, Vision, Ocular*, Visual Perception*, Animals, Convolutional Neural Networks, Photic Stimulation, Retina (* major topic)
Topic: Advanced Memory and Neural Computing (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Funding: Hong Kong Research Grants Council, Young Collaborative Research Grant (C5001-24), Research Institute for Smart Energy (U-CDC9); National Natural Science Foundation of China (National Science Foundation of China) (52373248)
Citations: not cited yet (Europe PMC); 61 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above.

Zenodo 20457125

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (5 files), Keras (4 files), TensorFlow (4 files), pandas (3 files), Matplotlib (2 files), OpenCV (2 files), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
7 files
At the source:

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: Zenodo 20457125

Read it in the paper: doi.org/10.1038/s41467-026-74769-4.

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;
  • 7 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41467-026-74769-4.

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 1 keyword, 8 MeSH terms, 2 funders, 60 references.

Cite

This paper

Zhai, Y., Chen, P., Luo, Y., Lv, Z., Ding, G., Yang, J., Zheng, M., Zhou, Y., Chai, Y., & Han, S.-T. (2026). Amphibian-inspired neuromorphic dynamic vision systems based on ferroelectric field-effect transistor. Nature communications, 17(1), 8641. https://doi.org/10.1038/s41467-026-74769-4

BibTeX

@article{zhai2026amphibian,
author = {Zhai, Yongbiao and Chen, Peijie and Luo, Ying and Lv, Ziyu and Ding, Guanglong and Yang, Junjie and Zheng, Minglin and Zhou, Ye and Chai, Yang and Han, Su-Ting},
title = {{Amphibian-inspired neuromorphic dynamic vision systems based on ferroelectric field-effect transistor}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {8641},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74769-4},
url = {https://doi.org/10.1038/s41467-026-74769-4},
pmid = {42336895},
pmcid = {PMC13486663}
}

RIS

TY - JOUR
AU - Zhai, Yongbiao
AU - Chen, Peijie
AU - Luo, Ying
AU - Lv, Ziyu
AU - Ding, Guanglong
AU - Yang, Junjie
AU - Zheng, Minglin
AU - Zhou, Ye
AU - Chai, Yang
AU - Han, Su-Ting
TI - Amphibian-inspired neuromorphic dynamic vision systems based on ferroelectric field-effect transistor
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/23
VL - 17
IS - 1
SP - 8641
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74769-4
UR - https://doi.org/10.1038/s41467-026-74769-4
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Amphibian-inspired neuromorphic dynamic vision systems based on ferroelectric field-effect transistor",
"container-title": "Nature communications",
"author": [
{
"family": "Zhai",
"given": "Yongbiao"
},
{
"family": "Chen",
"given": "Peijie"
},
{
"family": "Luo",
"given": "Ying"
},
{
"family": "Lv",
"given": "Ziyu"
},
{
"family": "Ding",
"given": "Guanglong"
},
{
"family": "Yang",
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},
{
"family": "Zheng",
"given": "Minglin"
},
{
"family": "Zhou",
"given": "Ye"
},
{
"family": "Chai",
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{
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"given": "Su-Ting"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "8641",
"DOI": "10.1038/s41467-026-74769-4",
"PMID": "42336895",
"PMCID": "PMC13486663",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74769-4",
"language": "en",
"issued": {
"date-parts": [
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23
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]
}
}

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