superconducting-neuron-neuromorphic

name: superconducting-neuron-neuromorphic description: "Programmable superconducting neuron with intrinsic in-memory computation and dual-timescale plasticity for ultra-efficient neuromorphic computing using Josephson junctions." tags: [superconducting-neuron, josephson-junction, neuromorphic-hardware, in-memory-computation, dual-timescale-plasticity, cryogenic-computing]

Programmable Superconducting Neuron for Neuromorphic Computing

Paper Information

• Title: Programmable superconducting neuron with intrinsic in-memory computation and dual-timescale plasticity for ultra-efficient neuromorphic computing
• Authors: Muen Wang, Shucheng Yang, Yuxiang Lin, Yuntian Gao, Xue Zhang, Xiaoping Gao, Minghui Niu, Huanli Liu, Yikang Wan, Wei Peng, Jie Ren
• arXiv ID: 2603.04966v2
• Published: 2026-03-05
• PDF: https://arxiv.org/pdf/2603.04966v2

Core Innovation

A programmable Josephson-junction-based leaky integrate-and-fire (LIF) neuron that unifies:

1. Programmability
2. Local memory
3. Multi-timescale plasticity

All in a single superconducting unit.

Key Advantages of Superconducting Neuromorphic Computing

• Ultra-high speed: Operates at cryogenic frequencies
• Low power dissipation: Near-zero resistance in superconducting state
• Event-driven efficiency: Only consumes power during switching

Neuron Architecture

Josephson-Junction-Based LIF Neuron

Components

• Josephson junctions: Provide nonlinearity and switching
• Bias currents: Encode somatic and synaptic parameters
• Inductive elements: Provide integration dynamics

Programmability

Somatic and synaptic parameters encoded directly in bias currents:

• Threshold voltage
• Leak rate
• Synaptic weights

Dual-Timescale Plasticity

Fast Timescale: Picosecond-Scale

• Mechanism: Short-term modulation of spike transmission
• Function: Rapid temporal adaptation
• Application: Real-time signal processing

Slow Timescale: Long-Term

• Retention: Exceeding 10,000 seconds (>2.7 hours)
• Function: Robust weight storage
• Application: Long-term memory

Performance Specifications

Operating Characteristics

Comparison

• Speed: Orders of magnitude faster than biological neurons
• Energy: Orders of magnitude more efficient than CMOS

SNN Implementation

Crossbar-Based Architecture

        Pre-synaptic neurons
              ↓
    ┌─────────────────────┐
    │   Synaptic crossbar │ ← Superconducting weights
    │   (Josephson array) │
    └─────────────────────┘
              ↓
        Post-synaptic neurons
    (Programmable LIF units)

Demonstrated Tasks

• Pattern recognition
• Temporal sequence learning
• Associative memory

Physical Implementation

Josephson Junction Physics

I = I_c · sin(φ)
where:
- I_c = critical current
- φ = phase difference across junction

Neuron Dynamics

τ · dV/dt = -V + I_syn + I_bias
if V > V_threshold:
    emit_spike()
    V = V_reset

Advantages

1. Unified design: Computation, memory, and plasticity in one unit
2. Programmable: Bias-current-based parameter setting
3. Fast: Picosecond-scale dynamics
4. Efficient: Femtojoule energy per spike
5. Multi-state: 10 threshold × 20 synaptic states

Challenges

1. Cryogenic operation: Requires cooling to millikelvin temperatures
2. Integration density: Current fabrication limits
3. Interface: Connecting to room-temperature systems
4. Scalability: Wafer-scale integration

Applications

1. High-frequency signal processing: Radar, communications
2. Quantum-classical interface: Bridging quantum and classical computing
3. Neuromorphic accelerators: Ultra-fast pattern recognition
4. Cryogenic AI: Space applications, quantum computing control

Related Work

• Josephson junction computing
• SFQ (Single Flux Quantum) logic
• Cryogenic CMOS
• Superconducting quantum computing

Citation

@article{wang2026superconducting,
  title={Programmable superconducting neuron with intrinsic in-memory computation and dual-timescale plasticity for ultra-efficient neuromorphic computing},
  author={Wang, Muen and Yang, Shucheng and Lin, Yuxiang and others},
  journal={arXiv preprint arXiv:2603.04966},
  year={2026}
}

Activation Keywords

• superconducting neuron
• Josephson junction LIF
• cryogenic neuromorphic
• dual-timescale plasticity
• femtojoule computing
• in-memory superconducting