Skip to main content
archive
Search Submit Donate Log in
Press Enter to search · Advanced search

Computer Science > Machine Learning

arXiv:2604.09943 (cs)
[Submitted on 10 Apr 2026]

Title:Vestibular reservoir computing

Authors:Smita Deb, Shirin Panahi, Mulugeta Haile, Ying-Cheng Lai
View a PDF of the paper titled Vestibular reservoir computing, by Smita Deb and 3 other authors
View PDF HTML (experimental)
Abstract:Reservoir computing (RC) is a computational framework known for its training efficiency, making it ideal for physical hardware implementations. However, realizing the complex interconnectivity of traditional reservoirs in physical systems remains a significant challenge. This paper proposes a physical RC scheme inspired by the biological vestibular system. To overcome hardware complexity, we introduce a designed uncoupled topology and demonstrate that it achieves performance comparable to fully coupled networks. We theoretically analyze the difference between these topologies by deriving a memory capacity formula for linear reservoirs, identifying specific conditions where both configurations yield equivalent memory. These analytical results are demonstrated to approximately hold for nonlinear reservoir systems. Furthermore, we systematically examine the impact of reservoir size on predictive statistics and memory capacity. Our findings suggest that uncoupled reservoir architectures offer a mathematically sound and practically feasible pathway for efficient physical reservoir computing.
Comments: 24 pages, 11 figures
Subjects: Machine Learning (cs.LG); Chaotic Dynamics (nlin.CD); Data Analysis, Statistics and Probability (physics.data-an)
Cite as: arXiv:2604.09943 [cs.LG]
  (or arXiv:2604.09943v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2604.09943
arXiv-issued DOI via DataCite

Submission history

From: Ying-Cheng Lai [view email]
[v1] Fri, 10 Apr 2026 22:47:41 UTC (3,271 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Vestibular reservoir computing, by Smita Deb and 3 other authors
  • View PDF
  • HTML (experimental)
  • TeX Source
license icon view license

Current browse context:

cs.LG
< prev   |   next >
new | recent | 2026-04
Change to browse by:
cs
nlin
nlin.CD
physics
physics.data-an

References & Citations

  • INSPIRE HEP
  • NASA ADS
  • Google Scholar
  • Semantic Scholar
Loading...

BibTeX formatted citation

Data provided by:

Bookmark

BibSonomy Reddit

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)
  • Author
  • Venue
  • Institution
  • Topic

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
We gratefully acknowledge support from our major funders, member institutions, , and all contributors.
About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)
Major funding support from
Simons Foundation Simons Foundation International Schmidt Sciences