# Closed-form control with spike coding networks

arXiv: https://arxiv.org/abs/2212.12887 · HTML: https://mlsanity.com/paper/2212.12887/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 25 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [The brain as an efficient and robust adaptive learner](https://mlsanity.com/paper/1705.08031/) (2017)
- [Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors](https://mlsanity.com/paper/2102.06408/) (2021)
- [Learning to represent signals spike by spike](https://mlsanity.com/paper/1703.03777/) (2017)
- [Real-Time Cortical Simulation on Neuromorphic Hardware](https://mlsanity.com/paper/1909.08665/) (2019)
- [Neural optimal feedback control with local learning rules](https://mlsanity.com/paper/2111.06920/) (2021)
- [Local dendritic balance enables learning of efficient representations in networks of spiking neurons](https://mlsanity.com/paper/2010.12395/) (2020)
- [Many-Joint Robot Arm Control with Recurrent Spiking Neural Networks](https://mlsanity.com/paper/2104.04064/) (2021)
- [Learning universal computations with spikes](https://mlsanity.com/paper/1505.07866/) (2015)
- [Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization](https://mlsanity.com/paper/2109.11045/) (2021)
- [Nonlinear computations in spiking neural networks through multiplicative synapses](https://mlsanity.com/paper/2009.03857/) (2020)

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

- [Neuromorphic Attitude Estimation and Control](https://mlsanity.com/paper/2411.13945/) (2024)
- [Enhancing Energy Efficiency and Reliability in Autonomous Systems Estimation using Neuromorphic Approach](https://mlsanity.com/paper/2307.07963/) (2023)
- [A Cloud-Edge Framework for Energy-Efficient Event-Driven Control: An Integration of Online Supervised Learning, Spiking Neural Networks and Local Plasticity Rules](https://mlsanity.com/paper/2405.02316/) (2024)
- [Spiking Neural Networks for Continuous Control via End-to-End Model-Based Learning](https://mlsanity.com/paper/2509.05356/) (2025)

## Abstract (arXiv)

Efficient and robust control using spiking neural networks (SNNs) is still an open problem. Whilst behaviour of biological agents is produced through sparse and irregular spiking patterns, which provide both robust and efficient control, the activity patterns in most artificial spiking neural networks used for control are dense and regular -- resulting in potentially less efficient codes. Additionally, for most existing control solutions network training or optimization is necessary, even for fully identified systems, complicating their implementation in on-chip low-power solutions. The neuroscience theory of Spike Coding Networks (SCNs) offers a fully analytical solution for implementing dynamical systems in recurrent spiking neural networks -- while maintaining irregular, sparse, and robust spiking activity -- but it's not clear how to directly apply it to control problems. Here, we extend SCN theory by incorporating closed-form optimal estimation and control. The resulting networks work as a spiking equivalent of a linear-quadratic-Gaussian controller. We demonstrate robust spiking control of simulated spring-mass-damper and cart-pole systems, in the face of several perturbations, including input- and system-noise, system disturbances, and neural silencing. As our approach does not need learning or optimization, it offers opportunities for deploying fast and efficient task-specific on-chip spiking controllers with biologically realistic activity.