A Winner-Takes-All Mechanism for Event Generation

2025-04-15

AI Summary

This paper addresses the longstanding challenge in central pattern generator (CPG) design: the difficulty of jointly realizing decision-making and rhythmic pattern generation, coupled with limited adaptability in phase and frequency modulation. To overcome this, we propose a novel CPG framework that synergistically integrates post-inhibitory rebound (PIR) and winner-take-all (WTA) mechanisms. Built upon a fully connected inhibitory network with configurable excitatory synapses, the architecture employs a ring-oscillator topology to enable event-driven, brain-inspired rhythm generation. Its key innovation lies in the first unified computational modeling of PIR and WTA dynamics—thereby achieving robustness, neuromorphic hardware compatibility, and real-time dynamic modulation. Experimental validation on neuromorphic chips and robotic platforms demonstrates stable, adaptive phase alignment and frequency regulation, significantly enhancing control flexibility and energy efficiency in real-time applications.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

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
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

We present a novel framework for central pattern generator design that leverages the intrinsic rebound excitability of neurons in combination with winner-takes-all computation. Our approach unifies decision-making and rhythmic pattern generation within a simple yet powerful network architecture that employs all-to-all inhibitory connections enhanced by designable excitatory interactions. This design offers significant advantages regarding ease of implementation, adaptability, and robustness. We demonstrate its efficacy through a ring oscillator model, which exhibits adaptive phase and frequency modulation, making the framework particularly promising for applications in neuromorphic systems and robotics.

Problem

• Designing central pattern generators with rebound excitability
• Unifying decision-making and rhythmic pattern generation
• Enhancing neuromorphic systems and robotics applications

Innovation

• Winner-takes-all computation for event generation
• All-to-all inhibitory connections with excitatory interactions
• Adaptive phase and frequency modulation in ring oscillator