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Spike-Native Transport Protocol v2
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Move spikes as spikes — preserving event identity, causal ordering, and bounded timing — with measurable degradation under noise.
SpikeLink defines a formal transport contract for spike-native computation operating inside conventional digital infrastructure.
Neuromorphic processors (Loihi, Akida, SpiNNaker, etc.) compute using:
- Asynchronous spikes
- Temporal coding
- Sparse event streams
The moment spikes leave the chip, they are typically forced through:
SPIKE → ADC → BITS → PACKET → BITS → DAC → SPIKE
This collapses temporal semantics, introduces distortion, and creates cliff-edge failure modes.
SPIKE → SPIKELINK → SPIKE
No forced analog collapse. No hidden timing drift. No semantic destruction.
Transport preserves event structure.
┌───────────────┐ ┌────────────────┐ ┌───────────────┐
│ Neuromorphic │ SPIKE │ SpikeLink │ SPIKE │ Neuromorphic │
│ Core (A) ├────────►│ Encoder ├────────►│ Core (B) │
│ │ │ + Contract │ │ │
└───────────────┘ └────────────────┘ └───────────────┘
│
▼
Bounded timing
Causal monotonicity
Count preservation
SpikeLink packets carry event identity, not reconstructed waveforms.
SpikeLink enforces invariants:
- Event count preservation
- Causal ordering monotonicity
- Explicit timing bounds
- Deterministic reconstruction
- Monotonic degradation under noise
Formal specification:
docs/transport_contract.md
This is a declared contract — not a best-effort approximation.
Absolute time encoding:
chunk_start_10us
Resolution: 10 μs
Two modes:
| Mode | Behavior |
|---|---|
| Timing-Exact | Preserves timing within quantization floor |
| Chunked | Fewer packets, bounded intra-chunk distortion |
Trade-offs are explicit and measurable.
spikelink/
├── api.py → Public interface
├── core/ → Codec + invariants
├── v2/ → Timing engine
├── types/ → SpikeTrain + packet structs
├── adapters/ → Neo, PyNN, NEST, Lava, etc.
├── verification/ → Contract enforcement suite
├── stress/ → Noise injection + degradation tests
├── hw/ → Hardware reference hooks
└── waveml/ → Wave-native integration layer
Verification and degradation modeling are first-class components.
| Adapter | Target | Status |
|---|---|---|
| Neo | EBRAINS | ✅ |
| Brian2 | Simulator | ✅ |
| Tonic | Event datasets | ✅ |
| PyNN | Multi-backend | ✅ |
| NEST | Simulator | ✅ |
| Nengo | Framework | ✅ |
| Lava | Intel Loihi | ✅ |
| SpikeInterface | Electrophysiology | ✅ |
All adapters use seconds as canonical time base.
pip install spikelinkOptional integrations:
pip install spikelink[neo]
pip install spikelink[pynn]
pip install spikelink[nest]
pip install spikelink[nengo]
pip install spikelink[lava]
pip install spikelink[spikeinterface]
pip install spikelink[full]from spikelink import SpikeTrain, SpikelinkCodec
train = SpikeTrain(times=[0.1, 0.2, 0.3, 0.4, 0.5])
codec = SpikelinkCodec()
packets = codec.encode_train(train)
recovered = codec.decode_packets(packets)
print(train.times)
print(recovered.times)from spikelink import VerificationSuite, DegradationProfiler
suite = VerificationSuite()
suite.print_results(suite.run_all())
profiler = DegradationProfiler()
profile = profiler.profile(noise_levels=[0, 0.1, 1.0, 10.0])
profiler.print_profile(profile)Degradation must be monotonic. Confidence must never inflate under noise.
SpikeLink does not compete with digital systems. It restores event semantics inside them.
Neuromorphic systems are event-native. Transport must be event-native.
- Extended channel models (loss, jitter, interference)
- FPGA packet handler reference path
- Distributed multi-node validation
- Cross-chip hardware benchmarks
Apache 2.0
Lightborne Intelligence
Event semantics over collapse. Coherence over distortion.