# A Neuromorphic Architecture for Scalable Event-Based Control

arXiv: https://arxiv.org/abs/2511.11924 · HTML: https://mlsanity.com/paper/2511.11924/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 79 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Builds on (most cited first, 15 of 15)

- [Spiking Control Systems](https://mlsanity.com/paper/2112.03565/) (2021)
- [Adapting to time: Why nature may have evolved a diverse set of neurons](https://mlsanity.com/paper/2404.14325/) (2024)
- [Neuromorphic Control](https://mlsanity.com/paper/2011.04441/) (2020)
- [Vector Symbolic Finite State Machines in Attractor Neural Networks](https://mlsanity.com/paper/2212.01196/) (2022)
- [Neuromorphic Control of a Pendulum](https://mlsanity.com/paper/2404.05339/) (2024)
- [Excitable behaviors](https://mlsanity.com/paper/1704.04989/) (2017)
- [Robust retrieval of dynamic sequences through interaction modulation](https://mlsanity.com/paper/2211.17152/) (2022)
- [Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware](https://mlsanity.com/paper/2405.01305/) (2024)
- [Online Optimization of Central Pattern Generators for Quadruped Locomotion](https://mlsanity.com/paper/2410.16417/) (2024)
- [Towards hardware Implementation of WTA for CPG-based control of a Spiking Robotic Arm](https://mlsanity.com/paper/2202.07064/) (2022)
- [On designing heteroclinic networks from graphs](https://mlsanity.com/paper/1302.0984/) (2013)
- [System identification of biophysical neuronal models](https://mlsanity.com/paper/2012.07691/) (2020)
- [Adaptive Conductance Control](https://mlsanity.com/paper/2204.08711/) (2022)
- [Antifragile control systems in neuronal processing: A sensorimotor perspective](https://mlsanity.com/paper/2404.14799/) (2024)
- [A Winner-Takes-All Mechanism for Event Generation](https://mlsanity.com/paper/2504.11374/) (2025)

## Abstract (arXiv)

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.