# NeuroPod: a real-time neuromorphic spiking CPG applied to robotics

arXiv: https://arxiv.org/abs/1904.11243 · HTML: https://mlsanity.com/paper/1904.11243/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [Versatile modular neural locomotion control with fast learning](https://mlsanity.com/paper/2107.07844/) (2021)
- [Neuromorphic adaptive spiking CPG towards bio-inspired locomotion of legged robots](https://mlsanity.com/paper/2101.09709/) (2021)
- [Spike-based computational models of bio-inspired memories in the hippocampal CA3 region on SpiNNaker](https://mlsanity.com/paper/2205.04782/) (2022)
- [Robust Iterative Value Conversion: Deep Reinforcement Learning for Neurochip-driven Edge Robots](https://mlsanity.com/paper/2408.13018/) (2024)
- [On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learning](https://mlsanity.com/paper/2504.08897/) (2025)

## Abstract (arXiv)

Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being designed to take a step further, by performing more complex tasks that have been carried out by smart animals or humans up to date. To this end, inspiration needs to be taken from biological examples. For instance, insects are able to optimally solve complex environment navigation problems, and many researchers have started to mimic how these insects behave. Recent interest in neuromorphic engineering has motivated us to present a real-time, neuromorphic, spike-based Central Pattern Generator of application in neurorobotics, using an arthropod-like robot. A Spiking Neural Network was designed and implemented on SpiNNaker. The network models a complex, online-change capable Central Pattern Generator which generates three gaits for a hexapod robot locomotion. Reconfigurable hardware was used to manage both the motors of the robot and the real-time communication interface with the Spiking Neural Networks. Real-time measurements confirm the simulation results, and locomotion tests show that NeuroPod can perform the gaits without any balance loss or added delay.