Vestibular reservoir computing

TL;DR

This paper introduces a biologically inspired physical reservoir computing scheme based on the vestibular system, demonstrating that uncoupled topologies can match the performance of fully coupled networks.

Contribution

It proposes an uncoupled reservoir topology inspired by biology, with theoretical analysis and empirical validation showing comparable performance to traditional coupled systems.

Findings

Uncoupled topology achieves similar memory capacity to fully coupled networks.

Theoretical memory capacity formula derived for linear reservoirs.

Reservoir size impacts predictive performance and memory capacity.

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.