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Polychronous Wave Computing: Timing-Native Address Selection in Spiking Networks

arXiv 2601.13079 ↗First posted 19 Jan 2026cond-mat.dis-nn cs.LG cs.NEcited by 0

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Abstract (arXiv)

Spike timing offers a combinatorial address space, suggesting that timing-based spiking inference can be executed as lookup and routing rather than as dense multiply--accumulate. Yet most neuromorphic and photonic systems still digitize events into timestamps, bins, or rates and then perform selection in clocked logic. We introduce Polychronous Wave Computing (PWC), a timing-native address-selection primitive that maps relative spike latencies directly to a discrete output route in the wave domain. Spike times are phase-encoded in a rotating frame and processed by a programmable multiport interferometer that evaluates K template correlations in parallel; a driven--dissipative winner-take-all stage then performs a physical argmax, emitting a one-hot output port. We derive the operating envelope imposed by phase wrapping and mutual coherence, and collapse timing jitter, static phase mismatch, and dephasing into a single effective phase-noise budget whose induced winner--runner-up margin predicts boundary-first failures and provides an intensity-only calibration target. Simulations show that nonlinear competition improves routing fidelity compared with noisy linear intensity readout, and that hardware-in-the-loop phase tuning rescues a temporal-order gate from 55.9% to 97.2% accuracy under strong static mismatch. PWC provides a fast routing coprocessor for LUT-style spiking networks and sparse top-1 gates (e.g., mixture-of-experts routing) across polaritonic, photonic, and oscillator platforms.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 8 references that are in the map. First posted on arXiv 19 Jan 2026.

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2 of 8 references.

References · 2

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  2. 2025Spiking Manifestocited by 4

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  1. 2026Determinism in the Undetermined: Deterministic Output in Charge-Conserving Continuous-Time Neuromorphic Systems with Temporal Stochasticity
  2. 2026Otters++: A Time-to-first-spike Based Energy Efficient Optical Spiking Transformer
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  4. 2024SpiDR: A Reconfigurable Digital Compute-in-Memory Spiking Neural Network Accelerator for Event-based Perception
  5. 2025Hardware-Software Collaborative Computing of Photonic Spiking Reinforcement Learning for Robotic Continuous Control
  6. 2025Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks
  7. 2025Delays in Spiking Neural Networks: A State Space Model Approach
  8. 2024A Fully-Configurable Open-Source Software-Defined Digital Quantized Spiking Neural Core Architecture