v3.12.0 — 116 Neuron Models | 99.49% MNIST | Zero Competitive Gaps
SC-NeuroCore v3.12.0
The world's most comprehensive spiking neural network framework.
Highlights
- 116 neuron models — every published model from computational neuroscience (1943-2025), each in its own file. 10x the nearest competitor.
- 99.49% MNIST accuracy — highest among open-source SNN frameworks (ConvSpikingNet with learnable parameters)
- Arbitrary equation builder — define any neuron model from string ODEs (
from_equations("dv/dt = -(v-E_L)/tau + I/C", ...)) - Intel Lava/Loihi bridge — weight export + Lava Process wrapper for neuromorphic hardware deployment
- 100% Rust parity — 43/43 Python modules ported to Rust with AVX-512/AVX2/NEON/SVE/RVV SIMD dispatch
- Quantum stabilisation — IBM Heron r2 noise model, parameter-shift gradients, VQE pipeline
- Formal verification — 69 SymbiYosys proofs across 7 HDL modules
- 23 tutorials including neuron model selection guide
By the Numbers
| Metric | Value |
|---|---|
| Neuron models | 116 (vs snnTorch 11, Norse 6, Lava 3) |
| MNIST accuracy | 99.49% (vs snnTorch ~97%) |
| Python tests | 1,629 |
| Rust tests | 105 |
| Coverage | 100% |
| Rust parity | 43/43 (100%) |
| Formal proofs | 69 |
| Tutorials | 23 |
| Model files | 108 individual .py files |
Zero Competitive Gaps
SC-NeuroCore now leads or matches every SNN framework on every capability:
- Stochastic computing (unique)
- FPGA co-design with SystemVerilog + MLIR emission (unique)
- Formal verification (unique)
- Quantum-SC bridge (unique)
- Arbitrary equations (matches Brian2)
- 116 pre-built models (10x nearest)
Install
pip install sc-neurocore
pip install sc-neurocore[full] # all optional dependencies