CogniSNN: An Exploration to Random Graph Architecture Based Spiking Neural Networks with Enhanced Depth-Scalability and Path-Plasticity Article Swipe
Yongsheng Huang
,
Peibo Duan
,
Zhipeng Liu
,
Kai Sun
,
Changsheng Zhang
,
Bin Zhang
,
Mingkun Xu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3233/faia251047
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3233/faia251047
Currently, most spiking neural networks (SNNs) still mimic the chain-like hierarchical architecture in traditional artificial neural networks (ANNs). This method significantly differs from random connections between neurons found in biological brains, limiting the ability to model the evolving mechanisms of neural pathways in biological neural systems, particularly in terms of dynamic depth-scalability and adaptive path-plasticity. This paper develops a new modeling paradigm for SNNs with random graph architecture (RGA), termed Cognition-aware SNN (CogniSNN). Furthermore, we model the depth-scalability and path-plasticity in CogniSNN by introducing a modified spiking residual neural node (ResNode) to counteract network degradation in deeper graph pathways, as well as a critical path-based algorithm that enables CogniSNN to perform path reusability on new tasks leveraging the features of the data and the RGA learned in old tasks. Experiments show that the performance of CogniSNN with redesigned ResNode is comparable, even superior, to current state-of-the-art SNNs on neuromorphic datasets. The critical path-based approach effectively achieves path reuse capability while maintaining expected performance in learning new tasks that are similar to or distinct from the old ones. This study showcases the potential of RGA-based SNNs and paves a new path for modeling the fusion of computational neuroscience and deep intelligent agents. The code is available at github.com/Yongsheng124/CogniSNN.
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https://doi.org/10.3233/faia251047Digital Object Identifier
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CogniSNN: An Exploration to Random Graph Architecture Based Spiking Neural Networks with Enhanced Depth-Scalability and Path-PlasticityWork title
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book-chapterOpenAlex work type
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2025Year of publication
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2025-10-21Full publication date if available
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Yongsheng Huang, Peibo Duan, Zhipeng Liu, Kai Sun, Changsheng Zhang, Bin Zhang, Mingkun XuList of authors in order
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https://doi.org/10.3233/faia251047Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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| abstract_inverted_index.Cognition-aware | 70 |
| abstract_inverted_index.path-plasticity | 79 |
| abstract_inverted_index.path-plasticity. | 54 |
| abstract_inverted_index.state-of-the-art | 145 |
| abstract_inverted_index.depth-scalability | 51, 77 |
| abstract_inverted_index.github.com/Yongsheng124/CogniSNN. | 206 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.831252 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |