reconfigurable-photonic-decision-network

name	reconfigurable-photonic-decision-network
description	Reconfigurable Nonlinear Photonic Decision Network (RNPDN) methodology for adaptive photonic neuromorphic computing. Local physical learning rules with tunable stability-plasticity tradeoff, controlled memory formation via bistable photonic states, and in-situ learning through driven-dissipative dynamics.

Reconfigurable Photonic Decision Network (RNPDN)

Methodology for adaptive photonic neuromorphic computing where computation, memory, and learning emerge directly from driven-dissipative dynamics in nonlinear optical systems.

Source

arXiv:2605.19911 — "Reconfigurable Nonlinear Photonic Networks for In-Situ Learning and Memory Formation via Driven-Dissipative Dynamics" Isaac Yorke Submitted: May 19, 2026 Subjects: Optics (physics.optics); Neural and Evolutionary Computing (cs.NE); Chaotic Dynamics (nlin.CD)

Core Problem

Most photonic neuromorphic implementations rely on fixed dynamical substrates (e.g., reservoir computing) where:

• Learning is restricted to external readout layers only
• Memory is limited to transient fading effects
• The physical layer cannot adapt intrinsically

RNPDN Framework

Key Properties

1. Local Physical Learning Rules: Adaptive state evolution driven by local interactions within the photonic network
2. Tunable Stability-Plasticity Tradeoff: Governed by decay and hysteresis mechanisms
3. Controlled Memory Formation/Erase: Via bistable photonic states
4. Fading Memory: Transient dynamics for temporal processing
5. In-Situ Learning: Intrinsic adaptation within the physical layer
6. Hardware-Faithful Nonlinear Dynamics: Incorporating saturation and dissipation

Architecture

Input → Nonlinear Photonic Nodes → Driven-Dissipative Dynamics → Output
                ↑                          ↓
        ← Local Learning Rules ← State Evolution
                ↑                          ↓
        ← Memory Formation (Bistability) ←

Driven-Dissipative Dynamics Model

The core dynamics combine:

• Driving term: Input signal injection
• Dissipation: Energy loss / decay mechanisms
• Nonlinearity: Saturation effects in photonic components
• Bistability: Two stable states for memory storage
• Hysteresis: State-dependent switching for stability-plasticity control

Local Learning Rule

def rnpsn_update(state, input_signal, decay_rate, learning_rate, 
                  hysteresis_threshold, noise=0.0):
    """
    Single-step update for RNPDN node.
    
    Args:
        state: Current node state (amplitude/phase)
        input_signal: External driving input
        decay_rate: Dissipation coefficient (controls fading memory)
        learning_rate: Adaptation strength
        hysteresis_threshold: Bistability switching threshold
        noise: Stochastic perturbation
    
    Returns:
        new_state: Updated node state
    """
    # Dissipation term
    dissipation = -decay_rate * state
    
    # Nonlinear driving (with saturation)
    drive = learning_rate * input_signal * (1 - state**2)
    
    # Hysteresis-based bistability
    if abs(state) > hysteresis_threshold:
        # Lock into stable state (memory formation)
        drive *= 0.1  # Reduced plasticity when in stable state
    
    # Stochastic perturbation (optional)
    stochastic = noise * np.random.randn()
    
    new_state = state + dissipation + drive + stochastic
    return np.tanh(new_state)  # Bounded output

Application Scenarios

Photonic Neuromorphic Hardware

• Design energy-efficient photonic computing systems
• Implement in-situ learning without external training loops
• Build adaptive optical signal processors

Temporal Pattern Recognition

• Leverage fading memory for sequence processing
• Use bistable states for persistent feature storage
• Apply to real-time signal classification tasks

Adaptive Control Systems

• Tunable stability-plasticity for dynamic environments
• In-situ adaptation without stopping operation
• Energy-efficient edge computing with photonic substrates

Key Advantages Over Traditional Approaches

Property	Reservoir Computing	RNPDN
Learning scope	Readout only	In-situ physical layer
Memory type	Transient only	Transient + persistent
Adaptation	No	Yes (local rules)
Stability-plasticity	Fixed	Tunable

Implementation Considerations

• Hardware constraints: Must account for actual photonic component nonlinearities
• Saturation limits: Physical components have bounded response ranges
• Thermal effects: Dissipation generates heat; may affect stability
• Scalability: Network topology design affects learning efficiency

Pitfalls

• Bistability threshold tuning is critical — too low causes instability, too high prevents adaptation
• Noise can disrupt memory formation; balance stochastic perturbation carefully
• Decay rate must be tuned per application: fast decay for temporal processing, slow decay for memory
• Numerical simulation must faithfully capture hardware nonlinearities

Activation

photonic neuromorphic computing, nonlinear optical networks, in-situ learning, driven-dissipative dynamics, adaptive photonics, bistable memory, neuromorphic hardware, optical computing

Related Skills

• physical-foundation-models
• phys-mcp-physical-neural-networks
• stochastic-physical-neural-networks