Neuromorphic Controllers Overview

• Neuromorphic controllers are event-driven systems that leverage spiking neural circuits for adaptive, low-latency, and energy-efficient control across dynamic applications.
• They integrate asynchronous spiking computation with real-time feedback to robustly regulate systems in fields such as robotics, human-machine interaction, and medical devices.
• Design trade-offs involve balancing spike frequency, energy use, and control precision using hybrid dynamical models to ensure practical stability and responsiveness.

Neuromorphic controllers are event-driven control systems that implement feedback and adaptation using principles derived from biological neural circuits, most commonly realized via networks of spiking neurons or asynchronous event-based modules. Unlike conventional sampled-data controllers that rely on periodic execution and continuous signals, neuromorphic controllers encode, process, and actuate based on discrete events—either spikes or hardware-based impulses—leading to energy and latency advantages, exceptional scalability, and distinctive input–output properties (Petri et al., 14 Nov 2025, Schmetterling et al., 8 Apr 2024, Ribar et al., 2020). This paradigm enables real-time, adaptive, and robust operation across domains such as robotics, process control, human-machine interaction, and medical devices.

1. Fundamental Principles and Architectures

Neuromorphic controllers generally instantiate three core principles: event-driven sensing, asynchronous spiking computation, and spike-based actuation. The canonical hardware building block is the leaky integrate-and-fire (LIF) neuron, whose membrane potential integrates error signals (or state variables) and emits a spike when crossing a threshold (Petri et al., 14 Nov 2025, Petri et al., 10 Sep 2024, Ribar et al., 2020). Spikes are employed to trigger actuation, communicate state changes, or drive parameter update mechanisms. Controllers may be structured as isolated neuron pairs emulating proportional or proportional-integral control (Petri et al., 14 Nov 2025), as mixed-feedback network motifs with positive and negative feedback loops operating at distinct timescales (Ribar et al., 2020), or as high-dimensional spiking neural networks (SNNs) embedding advanced control policies and regression models (Stroobants et al., 21 Nov 2024, Amaya et al., 13 Mar 2024, Wang et al., 17 Apr 2025).

Typical neuromorphic architectures include:

• Single-pair I&F networks stabilizing LTI plants via pulse-modulated control (Petri et al., 14 Nov 2025, Petri et al., 10 Sep 2024)
• Spiking PID controllers using input-weighted threshold adaptation or spike-based adder microcircuits for classical control law emulation (Stroobants et al., 2023, Stroobants et al., 2021, Pinero-Fuentes et al., 2022)
• Rhythmic automata (half-center oscillators, central pattern generators) for periodic motion control (Schmetterling et al., 8 Apr 2024, Petri et al., 8 Apr 2025, Ribar et al., 2020)
• Event-driven SNNs for inverse dynamics and high-DOF regression tasks (liquid state machines + MLP) in complex robotics (Wang et al., 17 Apr 2025, Amaya et al., 13 Mar 2024, Polykretis et al., 2022)
• Event-based control pipelines integrating neuromorphic vision sensors and SNN processing for perception-direct control (Paredes-Vallés et al., 2023, Hoffmann et al., 31 Jan 2025, Stroobants et al., 21 Nov 2024)

2. Formal Modeling and Analysis

Neuromorphic controllers are naturally described as hybrid dynamical systems, combining continuous-time plant evolution between spikes with discrete event-triggered jumps in actuator state or control variable (Medvedeva et al., 11 Jun 2025, Petri et al., 8 Apr 2025, Petri et al., 10 Sep 2024). For single-input SISO plants, the hybrid model comprises flow maps for state and neuron potentials, jump maps for spike-triggered resets and control actions, and dwell-time bounds ensuring no Zeno behavior (Petri et al., 10 Sep 2024).

Emulation-based design provides closed-loop practical stability guarantees by matching neuron parameters to classical controller gains and bounding the deviation in a special “spike-integral” norm (Petri et al., 14 Nov 2025). The property of integral spiking-input-to-state stability (iSISS) ensures that, for well-tuned neuron thresholds and amplitudes, the plant trajectory remains within an arbitrarily small tube of the ideal continuous-time solution with a trade-off between residual error and spike frequency. Lyapunov-based and contraction arguments yield rigorous bounds in these settings.

For rhythmic tasks, harmonic balance and describing function analysis predict the amplitude and period of limit cycles induced by periodic actuation events (Medvedeva et al., 11 Jun 2025, Petri et al., 8 Apr 2025); singular perturbation and fast–slow decomposition allow separation of event-driven adaptation from rapid plant dynamics.

3. Event-Driven Control Laws and Feedback Mechanisms

Feedback computation in neuromorphic controllers can be realized through direct integration of error signals (as in integrate-and-fire pairs for PI/PID control (Stroobants et al., 2023, Stroobants et al., 2021)), via population codes and winner-take-all circuits in SNN architectures (Zhao et al., 2020), or via state encoding in population-coded input layers driving deep recurrent networks (Stroobants et al., 21 Nov 2024, Amaya et al., 13 Mar 2024).

Controllers for rhythmic and oscillatory plants employ hybrid automata or neural oscillators, where actuation is triggered by precise, state-dependent events (e.g., zero-crossings of the pendulum angle or energy threshold crossings) (Schmetterling et al., 8 Apr 2024, Petri et al., 8 Apr 2025, Ribar et al., 2020). Adaptive mechanisms—implemented via slow adaptation spikes or linear filters—enable tracking of changing amplitude or frequency references through burst-width modulation or gain adaptation (Medvedeva et al., 11 Jun 2025, Schmetterling et al., 8 Apr 2024).

For multi-input, high-DOF tasks, liquid state machines and multilayer SNNs realize complex nonlinear regression and control policies, integrating temporal and spatial features of robot trajectories (Wang et al., 17 Apr 2025, Stroobants et al., 21 Nov 2024).

4. Practical Implementations and Hardware Platforms

Recent neuromorphic controller designs are realized both on mixed-signal neuromorphic hardware (Intel Loihi, DYNAP-SE), microcontroller-based SNN decks, and edge FPGA MPSoCs (Paredes-Vallés et al., 2023, Pinero-Fuentes et al., 2022, Stroobants et al., 2021, Zhao et al., 2020). Event-driven computation and population-coded state encoding minimize memory and computation requirements, yielding microsecond-scale loop latencies—even on 30 g microcontroller decks or ARM Cortex boards (Stroobants et al., 2023, Stroobants et al., 21 Nov 2024). Power consumption is typically in the sub-watt to milliwatt regime, due to sparse spike activity and local memory/computation (Amaya et al., 13 Mar 2024, Polykretis et al., 2022).

Controllers span applications in quadrotor altitude and attitude control (Stroobants et al., 21 Nov 2024, Stroobants et al., 2021), industrial robot force-torque feedback (Amaya et al., 13 Mar 2024), multi-DOF manipulators (Wang et al., 17 Apr 2025), embedded edge robotics (Pinero-Fuentes et al., 2022), event-based tactile sensing (Hoffmann et al., 31 Jan 2025), and closed-loop medical neuromodulation (Biswas et al., 25 Jul 2024).

Performance metrics reported include rise time (e.g., 0.8 s for quadrotor N-PID at 93 neurons (Stroobants et al., 2021)), steady-state localization error (e.g., 3.41 mm in tactile gesture (Hoffmann et al., 31 Jan 2025)), and energy per inference (e.g., 27 μJ for Loihi drone vision-control (Paredes-Vallés et al., 2023)). Neuromorphic controllers generally achieve comparable or superior tracking and robustness against disturbances versus conventional PID or ANN-based baselines, often with ≥40–96% lower energy consumption.

5. Applications and Demonstrated Results

• Robotics: Neuromorphic PID and SNN-based controllers realize real-time altitude control, attitude regulation, and object insertion in quadrotors and robot arms (Stroobants et al., 2021, Stroobants et al., 21 Nov 2024, Amaya et al., 13 Mar 2024, Wang et al., 17 Apr 2025, Polykretis et al., 2022).
• Human–machine interaction: Event-based touch interfaces and neuromorphic tactile sensors (NeuroTouch) achieve 91% gesture classification accuracy in low-latency AR/VR input (Hoffmann et al., 31 Jan 2025).
• Medical devices: LIF-based controllers in closed-loop deep brain stimulation reduce power consumption up to 56% and boost suppression efficiency by 6.77% over open-loop hardware (Biswas et al., 25 Jul 2024).
• Event-based vision and control: Fully neuromorphic pipelines for autonomous drone flight integrate event-based vision SNNs with spike-decoded control layers, achieving 200 Hz operation at 27 μJ per inference (Paredes-Vallés et al., 2023).
• Oscillatory and rhythmic systems: Hybrid I&F controllers robustly entrain pendula into prescribed limit cycles, with provable existence, uniqueness, and exponential stability (Petri et al., 8 Apr 2025, Schmetterling et al., 8 Apr 2024, Medvedeva et al., 11 Jun 2025).

6. Design Trade-offs, Limitations, and Scalability

Design of neuromorphic controllers is characterized by a trade-off between spike amplitude (precision), dwell-time (spike frequency), and the ultimate closed-loop error (Petri et al., 14 Nov 2025, Petri et al., 10 Sep 2024). Small thresholds and amplitudes yield finer control at the expense of higher spiking rates and energy cost. Event-based sensing and actuation lead to sparse power usage and robustness against delays and disturbances, but may require careful selection of event generation rules and adaptation filters to support wider operating regions or more complex plants (Medvedeva et al., 11 Jun 2025).

Current limitations include:

• Complexity in hand-tuning event thresholds, burst parameters, and adaptation gains, especially for high-dimensional or nonlinear systems.
• Scalability to large state spaces is limited by neuromorphic hardware I/O bandwidth and neuron counts, though event-driven architectures amortize these costs (Paredes-Vallés et al., 2023, Wang et al., 17 Apr 2025).
• Generalization of stability proofs to MIMO, nonlinear, and time-varying plants remains active research (Medvedeva et al., 11 Jun 2025).

7. Outlook and Future Research Directions

Recent research proposes formal frameworks for neuromorphic control based on hybrid systems, describing functions, and robust optimization to render these systems amenable to mature control-theoretical analysis (Medvedeva et al., 11 Jun 2025). Experimental validation on neuromorphic hardware, the extension to large-scale sensorimotor systems, systematic adaptation and online learning (e.g., STDP), and integration of full neuromorphic pipelines from sensing to actuation represent vibrant avenues (Stroobants et al., 21 Nov 2024, Paredes-Vallés et al., 2023, Amaya et al., 13 Mar 2024, Stroobants et al., 2021).

The convergence of sparse, asynchronous, and adaptive control laws with the physical instantiation in low-power chips points toward widespread deployment in ultra-fast, power-efficient, and robust autonomous agents, embodied robots, wearable medical devices, and sophisticated interaction systems.