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Formalizing Neuromorphic Control Systems: A General Proposal and A Rhythmic Case Study

Published: June 11, 2025 | arXiv ID: 2506.10203v1

By: Taisia Medvedeva, Alessio Franci, Fernando Castaños

Potential Business Impact:

Makes smart machines learn and control like brains.

Business Areas:
Embedded Systems Hardware, Science and Engineering, Software

Neuromorphic control is receiving growing attention due to the multifaceted advantages it brings over more classical control approaches, including: sparse and on-demand sensing, information transmission, and actuation; energy-efficient designs and realizations in neuromorphic hardware; event-based signal processing and control signal computation. However, a general control-theoretical formalization of what "neuromorphic control systems" are and how we can rigorously analyze, design, and control them is still largely missing. In this note, we suggest a possible path toward formalizing neuromorphic control systems. We apply the proposed framework to a rhythmic control case study and rigorously show how it has the potential to make neuromorphic control systems analysis and design amenable to mature control theoretical approaches like describing function analysis and harmonic balance, fast-slow analysis, discrete and hybrid systems, and robust optimization.

Page Count
21 pages

Category
Electrical Engineering and Systems Science:
Systems and Control