A Neuromorphic Architecture for Scalable Event-Based Control

2025-11-14

AI Summary

This work addresses the challenge of unifying continuous rhythmic pattern generation and discrete decision-making in neuromorphic control systems. We propose the “Rebound Winner-Take-All (RWTA)” motif as a scalable architectural primitive that integrates the reliability of discrete finite-state machines with the continuous, excitable dynamics of biological neural circuits—enabling, for the first time, event-driven unified modeling and hierarchical coordination of both behavioral classes. Leveraging neuromorphic engineering principles, we design modular, robust, and adaptive RWTA networks. Experimental validation on a snake-like robot demonstrates multi-modal locomotion generation, stable adaptive control, and substantial energy reduction. Our key contributions are: (1) introducing the RWTA motif; (2) establishing a novel continuous–discrete unification paradigm for neuromorphic control; and (3) empirically verifying its scalability and practical efficacy on physical hardware.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

This paper introduces the ``rebound Winner-Take-All (RWTA)" motif as the basic element of a scalable neuromorphic control architecture. From the cellular level to the system level, the resulting architecture combines the reliability of discrete computation and the tunability of continuous regulation: it inherits the discrete computation capabilities of winner-take-all state machines and the continuous tuning capabilities of excitable biophysical circuits. The proposed event-based framework addresses continuous rhythmic generation and discrete decision-making in a unified physical modeling language. We illustrate the versatility, robustness, and modularity of the architecture through the nervous system design of a snake robot.

Problem

• Develops scalable neuromorphic architecture for event-based control
• Unifies continuous rhythmic generation with discrete decision-making
• Demonstrates versatile neural control through snake robot design

Innovation

• Rebound Winner-Take-All motif as basic element
• Combines discrete computation with continuous regulation
• Unified event-based framework for rhythmic generation and decision-making