dynamic-neural-manifolds-control

name	dynamic-neural-manifolds-control
description	Dynamic neural manifolds methodology for flexible closed-loop control on neuromorphic hardware. Uses sensory inputs to modulate heterogeneous inhibition, gain, and transient currents, driving rapid subspace rotations to switch between behaviors and fine-grained trajectory control within them.
version	1.0.0
author	Hermes Agent
license	MIT
metadata	{"hermes":{"tags":["neuroscience","neuromorphic","dynamic-manifolds","closed-loop-control","spiking-neural-networks","spinnaker2","robotic-navigation","explainable-ai"],"category":"ai_collection","arxiv_id":"2607.07373","arxiv_url":"https://arxiv.org/abs/2607.07373","published":"2026-07-08","authors":["Oskar von Seeler","Christian Tetzlaff","Andrew Lehr"],"categories":["cs.NE"],"trigger_words":["dynamic neural manifolds","closed-loop control","neuromorphic hardware","spinnaker","subspace rotation","manifold geometry","behavior switching","trajectory control","spiking network","heterogeneous inhibition"]}}
created	2026-07-12
updated	2026-07-12

Dynamic Neural Manifolds for Flexible Closed-Loop Control on Neuromorphic Hardware

arXiv: 2607.07373 | Published: 2026-07-08 | Authors: Oskar von Seeler, Christian Tetzlaff, Andrew Lehr

Core Thesis

Sequential neural activity in biological circuits evolves along dynamic, low-dimensional manifolds to enable flexible behavior. This paper extends the dynamic neural manifold framework to neuromorphic engineering, implementing it on the SpiNNaker 2 chip for real-time, closed-loop control.

By allowing sensory inputs to modulate heterogeneous inhibition, gain, and transient currents, the architecture drives:

1. Rapid subspace rotations to switch between behaviors
2. Fine-grained trajectory control within subspaces

Key Concepts

Dynamic Neural Manifolds

• Biological neural activity doesn't explore the full high-dimensional state space; it evolves along low-dimensional manifolds
• These manifolds are dynamic — they can be reshaped by external inputs
• Specific circuit mechanisms link manifold geometry to computational function

Sensory Modulation Mechanisms

Three types of sensory input modulation enable manifold control:

1. Heterogeneous inhibition: Different neurons receive different levels of inhibitory input, reshaping the manifold's curvature
2. Gain modulation: Multiplicative scaling of neuronal responses changes the manifold's scale and direction
3. Transient currents: Brief current injections cause rapid manifold rotations

Subspace Rotations

When the agent needs to switch behaviors (e.g., from "explore" to "avoid"), sensory inputs trigger a rotation of the active subspace, redirecting the trajectory to a different behavioral attractor.

Implementation on SpiNNaker 2

Architecture

Sensory Input → [Inhibition Modulator, Gain Modulator, Transient Current Injector]
                    ↓
              Spiking Network (with dynamic manifold structure)
                    ↓
              Motor Output → Robotic Agent
                    ↓
              Sensory Feedback (closed loop)

Key Design Decisions

• Real-time operation: SpiNNaker 2 enables real-time spike processing
• Parameterizable manifolds: Circuit mechanisms are explicitly tied to manifold geometry