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Going deeper with morphologically detailed neural networks by simulation-based gradient propagation

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Paper

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The authors' code

Python · 75 lines · 2.7 KB · Apache-2.0

  1. import os
  2. import numpy as np
  3. import tensorflow as tf
  4. from resnet20 import make_resnet
  5. import foolbox
  7. dataset = 'mnist'
  8. learning_rate = 0.01
  9. lr_str = '1e-2'
  10. batch_size = 64
  11. epochs = 60
  12. load_trained = False
  13. attack_method = 'fgsm'
  14. base_model = 'res'
  17. def attack_model(model, method, bounds, x_test, y_test, epsilons):
  18. fmodel = foolbox.TensorFlowModel(model, bounds)
  19. clean_acc = foolbox.accuracy(fmodel, x_test, y_test)
  20. print(f"clean acc:{clean_acc:f}")
  22. if method == 'fgsm':
  23. attack = foolbox.attacks.FGSM()
  24. elif method == 'pgd':
  25. attack = foolbox.attacks.PGD()
  26. else:
  27. raise NotImplementedError(f'Unknown attack method: {method}')
  28. raw_advs, clipped_advs, success = attack(fmodel, x_test, y_test, epsilons=epsilons)
  29. robust_acc = 1 - success.numpy().mean(axis=-1)
  30. for eps, acc in zip(epsilons, robust_acc):
  31. print(f"eps:{eps:f} acc:{acc:f}")
  33. return clipped_advs
  36. def prepare_data(dataset):
  37. if dataset == 'mnist':
  38. from tensorflow.keras.datasets import mnist
  39. (x_train, y_train), (x_test, y_test) = mnist.load_data()
  40. x_train = x_train.astype('float32') / 255
  41. y_train = y_train.astype('int32')
  42. x_test = x_test.astype('float32') / 255
  43. y_test = y_test.astype('int32')
  44. x_train = np.expand_dims(x_train, -1)
  45. y_train = y_train.flatten()
  46. x_test = np.expand_dims(x_test, -1)
  47. y_test = y_test.flatten()
  48. else:
  49. raise NotImplementedError(f"Unknown dataset: {dataset}")
  51. return (x_train, y_train), (x_test, y_test)
  54. if __name__ == '__main__':
  55. (x_train, y_train), (x_test, y_test) = prepare_data(dataset)
  57. model = make_resnet(x_train.shape[1:], 10, 3, 1e-5)
  58. # model.summary()
  59. optim = tf.keras.optimizers.SGD(learning_rate, momentum=0.9, nesterov=True)
  60. model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), optimizer=optim, metrics=["acc"])
  61. if not load_trained:
  62. model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test))
  63. model.save_weights(f"resnet20_{dataset}_{epochs:d}epochs_sgd{lr_str}.h5")
  64. else:
  65. model.load_weights(f"resnet20_{dataset}_{epochs:d}epochs_sgd{lr_str}.h5")
  66. model.evaluate(x_test, y_test, batch_size=batch_size)
  68. epsilons = [0.02 * (i + 1) for i in range(10)]
  69. adv_img_list = attack_model(model, attack_method, (0, 1), tf.convert_to_tensor(x_test), tf.convert_to_tensor(y_test), epsilons)
  71. if not os.path.isdir(f"./{dataset}"):
  72. os.makedirs(f"./{dataset}")
  73. for adv_img, eps in zip(adv_img_list, epsilons):
  74. save_name = f"./{dataset}/{attack_method}{base_model}{eps:.2f}_test.npz"
  75. np.savez(save_name, x_test=adv_img.numpy(), y_test=y_test)

attack_resnet20.py at commit 8bdab07, under Apache-2.0 · at the source

Overview

Authors: Gan He1, Kai Du1, Tiejun Huang1,2
  1. National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China
  2. Beijing Academy of Artificial Intelligence, Beijing, China
Journal: Frontiers in computational neuroscience, volume 20, article 1904220
Dates: received 9 June 2026; accepted 18 August 2026; published online 10 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI · PMCID PMC13601319
Status: code verified
Categories: computational modeling (no new data) (modality), none (in silico) (organism), computational (subfield)
Methods: Single-unit activity, calcium imaging
Keywords: adversarial robustness, cable theory, dendritic computation, learning rule, multi-compartment model, synaptic plasticity
Funding: National Natural Science Foundation of China (National Science Foundation of China) (32471149)
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

Morphologically detailed dendrites possess powerful computational capabilities but are computationally expensive. Therefore, exploring how large-scale, detailed multi-compartment neural networks achieve learning proves challenging, presenting significant obstacles both in learning algorithms and simulation infrastructures. Here, we provide an extension to the DeepDendrite framework to enable the construction and data-driven training of multi-layer, detailed multi-compartment neural networks with highly modularized layer components. The gradient at each layer is computed by simulating gradient-mirror neurons in the feedback pathway simultaneously with the detailed neurons in the feedforward pathway, providing a simulation-based implementation of backpropagation in detailed neural networks. We demonstrate comparable results on classic image classification datasets with fully-connected and convolutional architectures. Furthermore, we analyze transfer attack robustness between artificial neural networks (ANNs), detailed neural networks and single-compartment networks. In conclusion, we provide a useful framework to investigate learning in multi-layer detailed neural networks, potentially offering further insights into exploiting the computational potential of dendrites at a large scale.

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

Repository

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

MS-GEB/DeepDendrite-modularization

License: Apache-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 8bdab07ff685984ea80a5ca171b60820ccbed4a7, 11 September 2026
Languages: C/C++ (463), C (392), NEURON (345), C++ (297), Python (154), Shell (63), Java (9), Perl (3), CUDA (3)
Size: 3,849 files, 1,729 scripts
Software Heritage: not archived
Found in: “Code availability statement”
Holds: README, license file, environment (src/nrn_modify/src/neuronmusic/setup.py, src/nrn_modify/src/nrnpython/setup.py, src/nrn_modify/share/lib/python/neuron/crxd/geometry3d/setup.py, src/nrn_modify/share/lib/python/neuron/rxd/geometry3d/setup.py), tests, documentation
Not found: CITATION.cff, continuous integration
Tools: NEURON (450 files), NumPy (90 files), Matplotlib (30 files), SciPy (7 files), Keras (2 files), TensorFlow (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
1,731 files
At the source: github.com/MS-GEB/DeepDendrite-modularization

Code availability statement

The source code for the modified DeepDendrite and the modularized layer APIs is available at https://github.com/MS-GEB/DeepDendrite-modularization.

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

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;
  • 1,729 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

Publicly available datasets were analyzed in this study. The MNIST dataset used in this study is available at http://yann.lecun.com/exdb/mnist. The CIFAR-10 dataset used in this study is available at https://www.cs.toronto.edu/~kriz/cifar.html. The source code for the modified DeepDendrite and the modularized layer APIs is available at https://github.com/MS-GEB/DeepDendrite-modularization.

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 6 keywords, 1 funder, 39 references.

Cite

This paper

He, G., Du, K., & Huang, T. (2026). Going deeper with morphologically detailed neural networks by simulation-based gradient propagation. Frontiers in computational neuroscience, 20, 1904220.

BibTeX

@article{he2026going,
author = {He, Gan and Du, Kai and Huang, Tiejun},
title = {{Going deeper with morphologically detailed neural networks by simulation-based gradient propagation}},
journal = {Frontiers in computational neuroscience},
year = {2026},
month = sep,
volume = {20},
pages = {1904220},
publisher = {Frontiers Media SA},
issn = {1662-5188},
pmcid = {PMC13601319}
}

RIS

TY - JOUR
AU - He, Gan
AU - Du, Kai
AU - Huang, Tiejun
TI - Going deeper with morphologically detailed neural networks by simulation-based gradient propagation
T2 - Frontiers in computational neuroscience
J2 - Front Comput Neurosci
PY - 2026
DA - 2026/09/25
VL - 20
SP - 1904220
SN - 1662-5188
PB - Frontiers Media SA
LA - en
ER -

CSL-JSON

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"type": "article-journal",
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"container-title": "Frontiers in computational neuroscience",
"author": [
{
"family": "He",
"given": "Gan"
},
{
"family": "Du",
"given": "Kai"
},
{
"family": "Huang",
"given": "Tiejun"
}
],
"container-title-short": "Front Comput Neurosci",
"volume": "20",
"page": "1904220",
"PMCID": "PMC13601319",
"ISSN": "1662-5188",
"publisher": "Frontiers Media SA",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
25
]
]
}
}

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