global-mean-amplitude-snn-cim

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Global mean-amplitude feedback-enhanced spiking neural network coherent ising machine (GFSNN-CIM) with physics-driven amplitude stabilization. Solves Max-Cut with 27% improvement vs conventional SNN-CIM, validated on traffic assignment problems. Based on Jiang, Ma, Wang & Wang (arXiv: 2509.13917). Use when solving combinatorial optimization with spiking neural networks, implementing coherent ising machines, or applying mean-amplitude feedback stabilization to SNN optimizers.

hiyenwong By hiyenwong schedule Updated 6/3/2026

name: global-mean-amplitude-snn-cim description: "Global mean-amplitude feedback-enhanced spiking neural network coherent ising machine (GFSNN-CIM) with physics-driven amplitude stabilization. Solves Max-Cut with 27% improvement vs conventional SNN-CIM, validated on traffic assignment problems. Based on Jiang, Ma, Wang & Wang (arXiv: 2509.13917). Use when solving combinatorial optimization with spiking neural networks, implementing coherent ising machines, or applying mean-amplitude feedback stabilization to SNN optimizers."

Global Mean-Amplitude Enhanced SNN Coherent Ising Machine

Physics-driven amplitude stabilization for spiking neural network-based coherent Ising machines. Based on Jiang et al. (arXiv: 2509.13917).

Core Methodology

  • Global mean-amplitude feedback enhances spiking neural network CIM
  • Physics-driven amplitude stabilization prevents oscillation divergence
  • 27% improvement in Max-Cut solution success rates vs conventional SNN-CIM
  • Validated on traffic assignment problems (generalizes beyond Max-Cut)

Architecture

  1. SNN encodes Ising model spins as neuron states
  2. Global mean-amplitude feedback stabilizes collective dynamics
  3. Physics-driven amplitude correction enforces energy landscape convergence
  4. Readout maps final spiking states to optimization solution

When to Use

  • Combinatorial optimization (Max-Cut, graph partitioning, traffic assignment)
  • Coherent Ising Machine implementations
  • Spiking neural network optimizers
  • Physics-inspired neural computation
  • Problems requiring amplitude stabilization in oscillatory networks

Key Advantages

  1. 27% success rate improvement over baseline SNN-CIM
  2. Generalizes beyond Max-Cut to real-world optimization
  3. Physics-driven (not learned) stabilization — no training required
  4. Compatible with existing SNN hardware implementations

Related Concepts

  • Coherent Ising Machines (CIM)
  • Spiking Neural Networks (SNN)
  • Combinatorial Optimization
  • Mean-Field Feedback
  • Amplitude Stabilization

Activation: coherent ising machine, GFSNN-CIM, mean-amplitude feedback, spiking neural optimizer, physics-driven stabilization, Max-Cut SNN, arXiv:2509.13917

Install via CLI
npx skills add https://github.com/hiyenwong/ai_collection --skill global-mean-amplitude-snn-cim
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