The DOI matches its Crossref record.
Translation-Equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors
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The path integration capability demonstrated in the neural circuit model builds upon well-established computational frameworks, and no additional robustness tests are performed in this regard.
“It should be noted that the path integration capability demonstrated here builds upon well-established computational frameworks [11,31], and thus we do not perform additional tests on its robustness in this regard.”
From Unfolding the Black Box of Recurrent Neural Networks for Path Integration (2025) · cites this paper as [31]
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Unfolding the Black Box of Recurrent Neural Networks for Path Integration
Chu, T., Wu, Y., Burgess, N., Ji, Z., & Wu, S. (2025) · Advances in Neural Information Processing Systems 38 · doi:10.52202/085713-3412 · Cited by 3
Self-citation
Metadata
- Authors
- Zhang, W., Wu, Y. N., & Wu, S.
- ORCID
- Venue
- Advances in Neural Information Processing Systems 35
- Published
- 2022
- Pages
- 15770-15783
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W7133244203
- Cited by
- 1
- References
- 0
- Citations by year
- 2026: 1
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- neural information processing systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Nonlinear Dynamics and Pattern Formation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- translation equivariance, recurrent neural networks, continuous attractor networks, spatial invariance
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