.png){kind=icon}
.png){kind=icon}
Return
Geometric perspectives on multi-input reservoir computing
.png){kind=icon}
OA
AI
DOI：10.1016/j.neunet.2026.108838.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A geometric framework for multi-input reservoir computing is developed. Channel-wise Gramians, principal angles, and a coupling index are used to characterise collapse, decoupling, and a nontrivial multimodal regime within a single recurrent system. Structured sparse input pathways are shown to realise this intermediate regime, and the analysis is extended to nonlinear tanh reservoirs through time-varying linearisations and local Gramians. CLIP-style contrastive experiments on synthetic paired features and on small paired data constructed from Flickr8k images by index alignment, together with a coupled chaotic benchmark based on two weakly interacting Lorenz systems, illustrate the three regimes. The results indicate that the nontrivial structured case yields a balanced internal multimodal geometry and competitive retrieval and multi-task prediction, while the proposed diagnostics provide an interpretable tool to analyse and design multi-input reservoir couplings.
Keywords:
Reservoir computing
Multimodal systems
Principal angles
Controllability gramians
Contrastive learning
68T05
93B05
15A18
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.3
Papers:
7.8K
Citations:
3.0W
Organization
No organization information available