A Spiking Ring-Attractor Architecture for Representation and Control of Revolute Robot Joints

Aung Htet; Alejandro Rodriguez Jimenez; Ruidong Ma; Alessandro Di Nuovo

cs.ROOct 19, 2026

A Spiking Ring-Attractor Architecture for Representation and Control of Revolute Robot Joints

Authors: Aung Htet, Alejandro Rodriguez Jimenez, Ruidong Ma, Alessandro Di Nuovo

Organizations: Sheffield Hallam University, Sheffield, UK

Abstract

Robotic systems with revolute joints evolve on circular state spaces, yet most control architectures represent these states using Euclidean variables that ignore this underlying geometry. We propose a fully spiking neural control architecture in which ring attractors encode joint states, enforcing representations consistent with the topology of revolute systems. Homeostatic regulation computes goal-state mismatch directly over these distributed representations, and opponent gain-modulated populations convert this mismatch into controlled phase shifts of the activity bump, implementing set-point regulation as motion on the ring. We show that the bump dynamics follow an SO(2) geometry, and that gain asymmetries induce a signed phase velocity on the ring. To interface distributed activity with scalar regulation signals, we derive an analytic Fourier phase decoder and implement its signed components using push-pull spiking populations. For multi-joint systems, we demonstrate that local ring-based Fourier features compose into product representations that linearly decode SO(3) orientations through forward kinematics. Experiments in NEST validate the full SO(2) regulation mechanism and the spiking product-feature decoder for lift, pitch, and yaw. Robot experiments on a Tiago arm show that decoded ring dynamics generate joint velocity commands, producing directed motion with joint-dependent accuracy. Together, these results establish a neuromorphic control framework in which representation, regulation, and action are unified within a ring-attractor substrate aligned with the geometry of revolute systems.

Figures & tables

Figure 1: Single-ring architecture for revolute joint control, composed of four modules. The goal ring encodes the desired joint phase as a localized bump; the state ring encodes the current joint phase. A Positional Fourier readout decodes each bump into a scalar phase estimate, which feeds a Hammel-style homeostatic comparator. The comparator drives opponent left-gain (LG) and right-gain (RG) populations, moving the ring in their preferred direction. A mismatch between goal and state produces a gain imbalance that moves the bump state along the ring; when the mismatch vanishes, the opponent signals become balanced and stabilizes the bump.

Figure 2: Analysis of the Fourier phase decoder. Top left: sine features $S_{m}(g)$ for Gaussian bump activity. Top right: Fourier decoders with $K=1,3,50$ modes reconstruct centered position with increasing accuracy. Bottom left: push–pull spiking implementation of the $K=1$ feature. Bottom right: mean $\pm$ standard deviation spike response across 10 repeats and 25 ring positions, showing a stable phase-dependent readout.

Figure 3: Robot-to-ring interface for the Tiago experiments. Each control step pairs a Tiago camera frame with the current spiking ring activity. The circular and unwrapped plots show the same population response, making the bump location and centroid visible during control. The timeline shows decoded gain-modulated commands moving the joint from start to target. Quantitative tracking results are reported in Figure 5 , with additional analyses in Figures 7 and 8 .

Figure 4: Single-ring $SO(2)$ homeostatic controller implemented in NEST where the ring attractor is simulated using 200 neurons. A : ring-attractor firing-rate heatmap with the decoded circular centroid overlaid in white. The cyan dotted line marks the start phase and the orange dotted line marks the goal phase. B : left- and right-gain raster plots throughout the simulation period (associated to the top graph). C : relative goal error, computed as circular distance to the goal divided by ring size $N$ , averaged over $t>30000\,\mathrm{ms}$ across ten runs per goal. The black dotted vertical line marks the starting point. D : left- and right-homeostasis neuron spike-count heatmaps.

Figure 5: Tiago single-joint control where the opponent gain-modulation activity is translated to the robot’s joint velocity. Left: Robot simulation snapshot demonstrating how the robot moves for specific joint index. Right: Robot simulation used to demonstrate error between goal state and target state of the ring attractor based single joint control.

Figure 6: Sawtooth-decoded output-ring index against the true target index for two slices of the joint configuration space ( $q_{1}$ fixed with $q_{2}$ varied; $q_{2}$ fixed with $q_{1}$ varied). Decoded and true traces closely overlap for lift, pitch, and yaw. Discontinuities are expected sawtooth wrap-around effects from the circular representation, quantified in Figure 9 . See Appendix Figure 10 for decoder activity.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 7: Left: Gain-population imbalance versus decoded velocity command across joints 0,3 and 5. Each point represents one recorded time step from the ring controller, showing an approximately linear increase in decoded velocity with the difference between right and left gain-population spike counts. Right: Settling time for joints 0, 3, and 5 under the ring and PID controllers. Settling time is defined as the first time the absolute tracking error remains below 0.05 rad for 5 consecutive samples; bars show mean ± 1 SD across trials.

Figure 8: Left: Absolute tracking error over time for joints 0, 3, and 5, shown for representative Ring and PID trials. The dashed line indicates the 0.05 rad tolerance. PID drops into this band quickly, while the Ring controller reduces error more slowly and reaches the same threshold later. Right: Velocity command over time for joints 0, 3, and 5, shown for representative Ring and PID trials. PID starts with a strong corrective command and rapidly decays toward zero, while the Ring controller applies smaller, smoother commands that vary gradually over a longer duration.

Figure 9: Circular index error for the two-ring forward-kinematic decoder under the same sweep conditions as Figure 6 . Across both sweeps and all three decoded orientation variables, the circular error remains small, confirming that the discontinuities in Figure 6 arise from the circular representation rather than decoder failure.

Figure 10: Spiking implementation of the two-ring forward-kinematic decoder in NEST. Top: signed product-feature population activity during a sweep of $q_{2}$ with $q_{1}$ fixed, showing systematic variation of first-order features ( $\cos q_{1},\sin q_{1},\cos q_{2},\sin q_{2}$ ) and second-order products ( $\cos q_{1}\cos q_{2}$ , etc.) with joint angle. Bottom: output ring profiles for lift, pitch, and yaw at representative $q_{2}$ values, showing structured population responses driven by the product-feature layer.

Neuron parameters for iaf_psc_alpha
Parameter	Symbol	Ring	Homeostatic	Gain
Firing threshold (mV)	$V_{\mathrm{th}}$	0.8	0.8	1.35
Membrane time constant (ms)	$\tau_{m}$	7.04	7.04	7.04
Membrane capacitance (pF)	$C_{m}$	$3.96{\times}10^{6}$	$3.96{\times}10^{6}$	$3.96{\times}10^{6}$
Excitatory synaptic time constant (ms)	$\tau_{\mathrm{syn}}^{\mathrm{ex}}$	27.0	75.0	27.0
Inhibitory synaptic time constant (ms)	$\tau_{\mathrm{syn}}^{\mathrm{in}}$	50.0	50.0	50.0

Appendix

Table 1: Spiking model parameters used for the ring, homeostatic comparator, and gain-modulation populations.

Continuous Attractor Neural Networks

See all 9 papers

Apr 15, 2026

Neuromorphic Spiking Ring Attractor for Proprioceptive Joint-State Estimation

Maintaining stable internal representations of continuous variables is fundamental for effective robotic control. Continuous attractor networks provide a biologically inspired mechanism for encoding such variables, yet neuromorphic realizations have rarely addressed proprioceptive estimation under resource constraints. This work introduces a spiking ring-attractor network representing a robot joint angle through self-sustaining population activity. Local excitation and broad inhibition support a stable activity bump, while velocity-modulated asymmetries drive its translation and boundary conditions confine motion within mechanical limits. The network reproduces smooth trajectory tracking and remains stable near joint limits, showing reduced drift and improved accuracy compared to unbounded models. Such compact hardware-compatible implementation preserves multi-second stability demonstrating a near-linear relationship between bump velocity and synaptic modulation.

Federica Ferrari, Flavia Davidhi, Bernard Maacaron +6

Istituto Italiano di Tecnologia, Genoa, Italy · Department of Neurosurgery, University Hospital Zürich, Switzerland · Institute of Neuroinformatics, Univ. of Zurich and ETH Zurich, Switzerland +3

Continuous Attractor Neural Networks Robot State Estimation Spiking Neural Networks Neuromorphic Computing Recurrent Neural Networks

Ask PDF

Jun 9, 2026

A Spiking Neural Architecture for Coordinating Arm and Locomotor Control

Spiking Neural Networks (SNNs) coupled with neuromorphic hardware offer energy-efficient solutions for humanoid robot control. However, existing SNN-based motor control systems address bipedal locomotion and arm control in isolation, leaving integrated control of both unaddressed. We present a spiking architecture that coordinates force-based arm control and bipedal locomotion in a simulated humanoid, using the Neural Engineering Framework (NEF) and Semantic Pointer Architecture (SPA). High-level action selection between locomotor and arm control is mediated by a biologically grounded spiking basal ganglia model. We validate the system through co-simulation of Nengo, for the neural control, and Isaac Sim, demonstrating successful target reaching, continuous digit drawing, path-following locomotion, and finally, switching between walking and arm control via basal ganglia disinhibition. To our knowledge, this is the first integrated spiking controller to combine bipedal locomotion and arm control on a full-scale humanoid platform. The full spike-based implementation enables future deployment on low-power neuromorphic hardware.

Lea Steffen, Kathryn Simone, Graeme Damberger +3

Centre for Theoretical Neuroscience Dept. of Systems Design Engineering University of Waterloo Waterloo, Canada · Applied Brain Research Waterloo, Canada · Dept. of Nanotechnology Engineering University of Waterloo Waterloo, Canada +1

Humanoid Robot Locomotion Humanoid Robot Control Whole-Body Control Spiking Neural Networks

Ask PDF

Jul 8, 2026

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

In biological circuits, sequential neural activity evolves along dynamic, low-dimensional manifolds to enable flexible behavior. Spiking network models link aspects of this sequential activity to features of manifold geometry through specific circuit mechanisms, making dynamic neural manifolds parameterizable, and thereby offering an explainable framework for neural computation. Extending this framework to neuromorphic engineering, we present an implementation on the SpiNNaker 2 chip for real-time, closed-loop control. By allowing sensory inputs to modulate heterogeneous inhibition, gain, and transient currents, our architecture drives rapid subspace rotations to switch between behaviors, as well as fine-grained trajectory control within them. We validate this via a robotic simulation where an agent uses sensory feedback to dynamically reconfigure its manifold geometry to navigate through a maze. Our results establish dynamic manifolds as a feasible approach for explainable neuromorphic architectures and a substrate for investigating biological neural dynamics.

Oskar von Seeler, Christian Tetzlaff, Andrew Lehr

Group of Computational Synaptic Physiology, Department of Neuro- and Sensory Physiology, University Medical Center Göttingen, Germany · Campus Institute Data Science, University of Göttingen, Germany · Circulant Labs, Bensheim, Germany

Robotic Control Neural Representation Geometry Spiking Neural Networks Neuromorphic Computing

Ask PDF

Abstract

Continuous Attractor Neural Networks

Explore similar work

Neuromorphic Spiking Ring Attractor for Proprioceptive Joint-State Estimation

A Spiking Neural Architecture for Coordinating Arm and Locomotor Control

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