Dynamic neural manifolds for flexible closed-loop control on neuromorphic hardware

2026-07-09 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

The research presents an implementation of dynamic neural manifolds on the SpiNNaker 2 neuromorphic chip, enabling flexible closed-loop control for autonomous systems. This architecture allows sensory inputs to modulate heterogeneous inhibition, gain, and transient currents, acting as "control knobs" to drive rapid subspace rotations for behavior switching and fine-grained trajectory control. The system was validated through a robotic simulation where an agent navigated a maze by dynamically reconfiguring its manifold geometry based on sensory feedback. The SpiNNaker 2 implementation, optimized for spike-based communication and memory efficiency using a circulant weight matrix and 20% connectivity for 500 neurons, consistently operates below the 1ms real-time threshold. This work establishes dynamic manifolds as a feasible and explainable approach for neuromorphic architectures, offering a substrate for investigating biological neural dynamics and efficient control.

Key takeaway

For AI Hardware Engineers developing autonomous systems, this work demonstrates a robust method for implementing explainable, adaptive control on neuromorphic platforms. You should consider dynamic neural manifolds for tasks requiring real-time behavioral switching and precise trajectory adjustments, utilizing SpiNNaker 2's efficiency. This approach offers a clear mapping between circuit mechanisms and high-level behavior, simplifying debugging and validation in complex robotic applications.

Key insights

Dynamic neural manifolds on neuromorphic hardware enable explainable, flexible closed-loop control by mapping circuit mechanisms to geometric properties.

Principles

• Neural manifolds offer an explainable AI framework.
• Heterogeneous inhibition reorients neural subspaces.
• Gain and transient currents control trajectory speed and curvature.

Method

Implement a ring network with asymmetric recurrent connectivity on SpiNNaker 2, using spike-based communication, sparse connections, and circulant weight matrices. Stream control parameters and spikes for real-time closed-loop operation.

In practice

• Use SpiNNaker 2 for energy-efficient control.
• Apply dynamic manifolds for robot navigation.
• Modulate gain/inhibition for behavior switching.

Topics

• Neuromorphic Computing
• SpiNNaker 2
• Neural Manifolds
• Closed-Loop Control
• Robotics
• Explainable AI

Best for: Research Scientist, AI Scientist, AI Hardware Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.