Vestibular reservoir computing

2026-04-10

AI Summary

This work addresses the challenge of efficiently implementing reservoir computing in physical hardware, which is hindered by the complex interconnectivity of conventional architectures. Inspired by the biological vestibular system, the authors propose a decoupled reservoir topology that substantially reduces hardware complexity while preserving performance comparable to fully connected networks. The study introduces vestibular-inspired principles into reservoir computing for the first time, derives an analytical expression for the memory capacity of linear reservoirs, and establishes the conditions under which the decoupled structure is equivalent to its fully connected counterpart. Theoretical insights are complemented by empirical validation demonstrating that the proposed architecture closely matches the memory capacity and prediction accuracy of fully connected reservoirs, even in nonlinear dynamical systems, thereby offering a promising pathway toward efficient physical realizations.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• reservoir computing
• hardware implementation
• interconnectivity
• vestibular system
• memory capacity

Innovation

• vestibular-inspired
• uncoupled topology
• reservoir computing
• memory capacity
• physical implementation