What does this research mean for the field?

Fully spiking actor-critic neural network policies trained via Proximal Policy Optimization outperform conventional artificial neural network controllers in high-speed, agile robotic tasks. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

What question did this study set out to answer?

To evaluate the feasibility and performance of a fully spiking actor-critic neural network trained via Proximal Policy Optimization for continuous control in high-speed quadcopter navigation.

September 10, 2026Unmanned Systems

Spiking Neural Networks for High-Speed Continuous Quadcopter Control Using Proximal Policy Optimization

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Authors

MCMichael Fernando Van Breukelen Castillo RFRobin Ferede RVReinier de Vos

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Overview

Experimental evaluation demonstrates superior high-speed flight performance of spiking neural networks in autonomous quadcopters, highlighting their potential for neuromorphic robotic control.

Key Points

To evaluate the feasibility and performance of a fully spiking actor-critic neural network trained via Proximal Policy Optimization for continuous control in high-speed quadcopter navigation.
Constructed a fully spiking actor-critic controller utilizing leaky integrate-and-fire neurons, surrogate gradient learning, and multi-cycle spike-rate decoding.
Trained the network using Proximal Policy Optimization (PPO) for continuous drone control in a gate-crossing task.
Benchmarked the spiking architecture against an artificial neural network in simulation and in 12-second real-world flight trials.
In 12-second real-world flight trials, the spiking network outperformed the artificial network in average reward (70.63 vs 59.77), mean velocity (7.94 vs 6.99 m/s), and gates cleared (46.33 vs 40.67).
Simulation tests showed the spiking neural network achieved higher episode rewards, greater robustness, and reduced crash rates compared to the artificial neural network.
Varying spike integration cycles revealed an operational trade-off, where moderate counts of 5 or 8 cycles delivered optimal output smoothness, task reward, and inference latency.

Cite This Study

Castillo et al. (2026) studied this question.

synapsesocial.com/papers/6aa27ae958559d80afc73cda https://doi.org/10.1142/s2301385027410147

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September 10, 2026Unmanned Systems

Spiking Neural Networks for High-Speed Continuous Quadcopter Control Using Proximal Policy Optimization

View Full Paper

Ask AI

Bookmark

Authors

MCMichael Fernando Van Breukelen Castillo RFRobin Ferede RVReinier de Vos

Discussion

Member takes

Overview

Experimental evaluation demonstrates superior high-speed flight performance of spiking neural networks in autonomous quadcopters, highlighting their potential for neuromorphic robotic control.

Key Points

To evaluate the feasibility and performance of a fully spiking actor-critic neural network trained via Proximal Policy Optimization for continuous control in high-speed quadcopter navigation.
Constructed a fully spiking actor-critic controller utilizing leaky integrate-and-fire neurons, surrogate gradient learning, and multi-cycle spike-rate decoding.
Trained the network using Proximal Policy Optimization (PPO) for continuous drone control in a gate-crossing task.
Benchmarked the spiking architecture against an artificial neural network in simulation and in 12-second real-world flight trials.
In 12-second real-world flight trials, the spiking network outperformed the artificial network in average reward (70.63 vs 59.77), mean velocity (7.94 vs 6.99 m/s), and gates cleared (46.33 vs 40.67).
Simulation tests showed the spiking neural network achieved higher episode rewards, greater robustness, and reduced crash rates compared to the artificial neural network.
Varying spike integration cycles revealed an operational trade-off, where moderate counts of 5 or 8 cycles delivered optimal output smoothness, task reward, and inference latency.

Cite This Study

Castillo et al. (2026) studied this question.

synapsesocial.com/papers/6aa27ae958559d80afc73cda https://doi.org/10.1142/s2301385027410147

View Full Paper

Ask AI

Bookmark