| name | dynamic-neural-manifolds-neuromorphic-control |
| description | Dynamic neural manifolds methodology for flexible closed-loop control on neuromorphic hardware. Uses ring attractor networks with sensory-modulated control neurons (speed, shape, selection) to drive subspace rotations and fine-grained trajectory control in neural state space. Implemented on SpiNNaker 2 chip with robotic maze navigation validation.
|
| version | 1.0.0 |
| author | Hermes Agent (from arXiv:2607.07373v1) |
| license | MIT |
| metadata | {"hermes":{"tags":["neuromorphic","neural-manifolds","closed-loop-control","spiking-networks","spinnaker2","ring-attractor"],"trigger_words":["dynamic neural manifold","neuromorphic control","spinnaker 2","ring network","closed-loop spiking","subspace rotation","neural trajectory","manifold geometry","sequential neural activity","flexible behavior","activity bump","control neurons"]},"paper":{"arxiv_id":"2607.07373v1","title":"Dynamic neural manifolds for flexible closed-loop control on neuromorphic hardware","authors":["Oskar von Seeler","Christian Tetzlaff","Andrew B. Lehr"],"published":"2026-07-08","categories":["cs.NE"]}} |
Dynamic Neural Manifolds for Neuromorphic Closed-Loop Control
Paper
- Title: Dynamic neural manifolds for flexible closed-loop control on neuromorphic hardware
- Authors: Oskar von Seeler, Christian Tetzlaff, Andrew B. Lehr
- Affiliation: University Medical Center Göttingen; Campus Institute Data Science, University of Göttingen; Circulant Labs
- arXiv: 2607.07373v1 [cs.NE] (2026-07-08)
- License: CC BY 4.0
Core Concept
In biological circuits, sequential neural activity evolves along dynamic, low-dimensional manifolds to enable flexible behavior. This paper presents a framework that makes these manifolds parameterizable through specific circuit mechanisms, enabling explainable neuromorphic control on real hardware.
Key Innovation
The architecture maps neural computation onto a geometric manifold framework where:
- Neural population activity = trajectory in N-dimensional state space
- Sequential activity = movement along low-dimensional curved manifold
- Control = modulation of manifold geometry (position, speed, shape) via simple neural mechanisms
Architecture
Ring Attractor Network
Input → Control Neurons → Ring Network → Readout → Motor Output
(3 types) (circulant) neurons (agent)
Ring Network: Circulant weight matrix with 50% connection sparsity (weights scaled 2x to compensate). Implements a ring attractor that produces a traveling "bump" of activity — a well-studied pattern in neuroscience (head direction cells, spatial navigation).
Three Control Neuron Types
| Control Type | Function | Biological Analogy |
|---|
| Speed control | Modulates propagation speed of activity bump around ring | Neuromodulatory gain control |
| Shape control | Changes width of activity bump (narrow ↔ broad) | Heterogeneous inhibition tuning |
| Selection control | Selectively inhibits subsets of ring neurons | Targeted inhibitory interneurons |
These three simple mechanisms serve as "control knobs" that allow sensory feedback to:
- Switch behavioral states (e.g., steering ↔ jumping) via subspace rotation
