Neuromorphic Spikes Unify Control and Decision Making

2026-02-06 · Source: Big Data & AI News - EE Times · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

Professor Rodolphe Sepulchre of the University of Cambridge, a control theorist, discusses how neuromorphic engineering can unify control and decision-making by leveraging the inherent continuous and discrete behaviors of biological neurons. In an episode of "Brains and Machines," Sepulchre explains that traditional control theory often separates the "plant" (machine) from the "controller" (intelligence), but his work focuses on the "nervous system" that interfaces them. He highlights how neural oscillator circuits can produce digital outputs from analog components, enabling event-based control, such as balancing a pendulum by pushing it at the right time. Sepulchre emphasizes the critical role of mixed positive and negative feedback mechanisms, found in every neuron, for both regulation (homeostasis) and decision-making (switching), a concept he believes was lost after the advent of digital computers. He illustrates this with a simulated snake model, where a hundred identical neurons with mixed feedback structures manage both muscle activation and high-level behavioral sequencing.

Key takeaway

For AI Scientists developing advanced robotic or autonomous systems, consider adopting neuromorphic principles to overcome the limitations of purely digital control. By integrating mixed positive and negative feedback mechanisms within your designs, you can create systems that inherently manage both continuous physical interactions and discrete decision-making, leading to more energy-efficient, robust, and biologically-inspired machines that interact with the environment "when needed, where needed."

Key insights

Neuromorphic spikes unify continuous and discrete control, enabling efficient, event-based machine intelligence.

Principles

• Mixed feedback is essential for both regulation and decision-making.
• Building neurons can be easier than simulating them.
• Sparsity in control interactions revolutionizes design.

Method

Design control systems by integrating mixed positive and negative feedback within neuromorphic architectures, allowing for event-based, multi-scale control that reconciles continuous physical interaction with discrete decision-making.

In practice

• Use event-based control for energy efficiency.
• Implement mixed feedback for integrated regulation and automation.
• Prioritize building over simulating for real-world robustness.

Topics

• Neuromorphic Engineering
• Control Theory
• Spiking Neural Networks
• Mixed Feedback Systems
• Event-Based Control

Best for: AI Scientist, AI Researcher, AI Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Big Data & AI News - EE Times.