# Neuromorphic Control

arXiv: https://arxiv.org/abs/2011.04441 · HTML: https://mlsanity.com/paper/2011.04441/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Augmenting learning in neuro-embodied systems through neurobiological first principles](https://mlsanity.com/paper/2407.04525/) (2024)
- [Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics](https://mlsanity.com/paper/2501.06762/) (2025)
- [A Winner-Takes-All Mechanism for Event Generation](https://mlsanity.com/paper/2504.11374/) (2025)
- [A Neuromorphic Architecture for Scalable Event-Based Control](https://mlsanity.com/paper/2511.11924/) (2025)

## Abstract (arXiv)

Neuromorphic engineering is a rapidly developing field that aims to take inspiration from the biological organization of neural systems to develop novel technology for computing, sensing, and actuating. The unique properties of such systems call for new signal processing and control paradigms. The article introduces the mixed feedback organization of excitable neuronal systems, consisting of interlocked positive and negative feedback loops acting in distinct timescales. The principles of biological neuromodulation suggest a methodology for designing and controlling mixed-feedback systems neuromorphically. The proposed design consists of a parallel interconnection of elementary circuit elements that mirrors the organization of biological neurons and utilizes the hardware components of neuromorphic electronic circuits. The interconnection structure endows the neuromorphic systems with a simple control methodology that reframes the neuronal control as an input-output shaping problem. The potential of neuronal control is illustrated on elementary network examples that suggest the scalability of the mixed-feedback principles.