Efficient and robust control with spikes that constrain free energy

2026-03-10

AI Summary

This work addresses the challenge of constructing brain-inspired control mechanisms that are both highly efficient and robust to internal and external perturbations while offering insights into neural system functioning. The authors propose a spiking neural network–based control framework that, for the first time, integrates the free energy principle with spiking activity: neurons fire only when such activity reduces the free energy of internal representations, thereby enabling sparse and efficient state estimation and action generation. This architecture achieves performance comparable to existing spiking control methods while significantly enhancing robustness against various disturbances—including sensory noise, physical collisions, synaptic noise, transmission delays, and neuronal silencing—offering a novel paradigm that bridges biological plausibility with engineering practicality for modeling biological systems and deploying neuromorphic hardware.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Bio-inspired Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Animal brains exhibit remarkable efficiency in perception and action, while being robust to both external and internal perturbations. The means by which brains accomplish this remains, for now, poorly understood, hindering our understanding of animal and human cognition, as well as our own implementation of efficient algorithms for control of dynamical systems.A potential candidate for a robust mechanism of state estimation and action computation is the free energy principle, but existing implementations of this principle have largely relied on conventional, biologically implausible approaches without spikes. We propose a novel, efficient, and robust spiking control framework with realistic biological characteristics. The resulting networks function as free energy constrainers, in which neurons only fire if they reduce the free energy of their internal representation. The networks offer efficient operation through highly sparse activity while matching performance with other similar spiking frameworks, and have high resilience against both external (e.g. sensory noise or collisions) and internal perturbations (e.g. synaptic noise and delays or neuron silencing) that such a network would be faced with when deployed by either an organism or an engineer. Overall, our work provides a novel mathematical account for spiking control through constraining free energy, providing both better insight into how brain networks might leverage their spiking substrate and a new route for implementing efficient control algorithms in neuromorphic hardware.

Problem

• spiking control
• free energy principle
• robustness
• neural efficiency
• biological plausibility

Innovation

• spiking neural networks
• free energy principle
• efficient control
• robustness
• neuromorphic computing