Towards Biologically Plausible Computing: A Comprehensive Comparison Article Swipe
Changze Lv
,
Yufei Gu
,
Zhengkang Guo
,
Zhibo Xu
,
Yixin Wu
,
Feiran Zhang
,
Tianyuan Shi
,
Zhenhua Wang
,
Ruicheng Yin
,
Yu Shang
,
Siqi Zhong
,
Xiaohua Wang
,
Muling Wu
,
Wenhao Liu
,
Tianlong Li
,
Jianhao Zhu
,
Cenyuan Zhang
,
Zixuan Ling
,
Xiaoqing Zheng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.16062
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.16062
Backpropagation is a cornerstone algorithm in training neural networks for supervised learning, which uses a gradient descent method to update network weights by minimizing the discrepancy between actual and desired outputs. Despite its pivotal role in propelling deep learning advancements, the biological plausibility of backpropagation is questioned due to its requirements for weight symmetry, global error computation, and dual-phase training. To address this long-standing challenge, many studies have endeavored to devise biologically plausible training algorithms. However, a fully biologically plausible algorithm for training multilayer neural networks remains elusive, and interpretations of biological plausibility vary among researchers. In this study, we establish criteria for biological plausibility that a desirable learning algorithm should meet. Using these criteria, we evaluate a range of existing algorithms considered to be biologically plausible, including Hebbian learning, spike-timing-dependent plasticity, feedback alignment, target propagation, predictive coding, forward-forward algorithm, perturbation learning, local losses, and energy-based learning. Additionally, we empirically evaluate these algorithms across diverse network architectures and datasets. We compare the feature representations learned by these algorithms with brain activity recorded by non-invasive devices under identical stimuli, aiming to identify which algorithm can most accurately replicate brain activity patterns. We are hopeful that this study could inspire the development of new biologically plausible algorithms for training multilayer networks, thereby fostering progress in both the fields of neuroscience and machine learning.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.16062
- https://arxiv.org/pdf/2406.16062
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400023438
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https://openalex.org/W4400023438Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2406.16062Digital Object Identifier
- Title
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Towards Biologically Plausible Computing: A Comprehensive ComparisonWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-23Full publication date if available
- Authors
-
Changze Lv, Yufei Gu, Zhengkang Guo, Zhibo Xu, Yixin Wu, Feiran Zhang, Tianyuan Shi, Zhenhua Wang, Ruicheng Yin, Yu Shang, Siqi Zhong, Xiaohua Wang, Muling Wu, Wenhao Liu, Tianlong Li, Jianhao Zhu, Cenyuan Zhang, Zixuan Ling, Xiaoqing ZhengList of authors in order
- Landing page
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https://arxiv.org/abs/2406.16062Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2406.16062Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2406.16062Direct OA link when available
- Concepts
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Computer science, Data scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.multilayer | 83, 207 |
| abstract_inverted_index.plausible, | 126 |
| abstract_inverted_index.predictive | 136 |
| abstract_inverted_index.propelling | 36 |
| abstract_inverted_index.questioned | 46 |
| abstract_inverted_index.supervised | 10 |
| abstract_inverted_index.algorithms. | 74 |
| abstract_inverted_index.cornerstone | 3 |
| abstract_inverted_index.development | 199 |
| abstract_inverted_index.discrepancy | 25 |
| abstract_inverted_index.empirically | 149 |
| abstract_inverted_index.plasticity, | 131 |
| abstract_inverted_index.biologically | 71, 78, 125, 202 |
| abstract_inverted_index.computation, | 56 |
| abstract_inverted_index.energy-based | 145 |
| abstract_inverted_index.neuroscience | 217 |
| abstract_inverted_index.non-invasive | 173 |
| abstract_inverted_index.perturbation | 140 |
| abstract_inverted_index.plausibility | 42, 92, 104 |
| abstract_inverted_index.propagation, | 135 |
| abstract_inverted_index.requirements | 50 |
| abstract_inverted_index.researchers. | 95 |
| abstract_inverted_index.Additionally, | 147 |
| abstract_inverted_index.advancements, | 39 |
| abstract_inverted_index.architectures | 156 |
| abstract_inverted_index.long-standing | 63 |
| abstract_inverted_index.Backpropagation | 0 |
| abstract_inverted_index.backpropagation | 44 |
| abstract_inverted_index.forward-forward | 138 |
| abstract_inverted_index.interpretations | 89 |
| abstract_inverted_index.representations | 163 |
| abstract_inverted_index.spike-timing-dependent | 130 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 19 |
| citation_normalized_percentile |