A Neuromorphic Architecture for Scalable Event-Based Control

TL;DR

This paper presents a scalable neuromorphic control architecture using the rebound Winner-Take-All motif, combining discrete and continuous computation for robust, versatile control exemplified by a snake robot.

Contribution

It introduces the rebound Winner-Take-All motif as a fundamental element for scalable neuromorphic control architectures, unifying discrete and continuous processes.

Findings

Demonstrates the architecture's robustness and modularity.

Shows effective control of a snake robot.

Addresses rhythmic generation and decision-making in a unified framework.

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.