sc-neurocore 3.14.0

Stochastic Computing SNN framework: NumPy + Rust SIMD + FPGA RTL.

© 1998–2026 Miroslav Šotek. All rights reserved. Contact: www.anulum.li | [e-mail] ORCID: https://orcid.org/0009-0009-3560-0851 License: GNU AFFERO GENERAL PUBLIC LICENSE v3 Commercial Licensing: Available

SC-NeuroCore

Version: 3.14.0 Status: 116 Neuron Models (107 Bio + 9 AI) | 99.49% MNIST (ConvSNN) | 2 928 Python tests passing (3 132 collected) + 378 Rust tests | 100% Core Coverage (322/535 files; optional/experimental excluded) | 109 Rust Neuron Models | 80-Model NetworkRunner

SC-NeuroCore is an open-source stochastic computing SNN framework with FPGA synthesis. 116 neuron models (107 biophysical + 9 AI-optimized) spanning 82 years of computational neuroscience (McCulloch-Pitts 1943 through ArcaneNeuron 2026) run inside a deterministic stochastic computing engine with bit-true Verilog RTL co-simulation, FPGA synthesis via an IR compiler (SystemVerilog + MLIR/CIRCT backends), an equation-to-Verilog compiler that turns arbitrary ODE strings into synthesizable Q8.8 fixed-point RTL, formal verification (7 SymbiYosys modules, 67 properties), a Rust SIMD engine 39–202× faster than Brian2 (27.7 billion synaptic events/s at 100K neurons, 109 Rust neuron models with PyO3 bindings, 80-model NetworkRunner with Rayon-parallel populations), CuPy GPU acceleration, JAX JIT training, MPI distributed simulation (billion-neuron scale via mpi4py), an identity continuity substrate (persistent spiking networks with checkpointing and L16 Director control), a 128-function spike train analysis toolkit (23 modules), 12 visualization plots, 13 advanced plasticity rules (pair/triplet/voltage STDP, BCM, BPTT, TBPTT, EWC, e-prop, R-STDP, MAML, STP, structural plasticity), 7 biological circuit primitives (gap junctions, tripartite synapse, Rall dendrite, cortical column, lateral inhibition, WTA, gamma oscillation), 10 model zoo configurations with 3 pre-trained weight sets, 9 hardware chip emulators, quantum hybrid computing (Qiskit + PennyLane + SC-to-quantum compiler), surrogate gradient training reaching 99.49% MNIST accuracy, a NIR bridge — FPGA backend for the neuromorphic intermediate representation standard (18/18 primitives, recurrent edges, multi-port subgraphs; verified interop with SpikingJelly, snnTorch, and Norse), a SpikeInterface adapter for experimental data import, ANN-to-SNN conversion (trained PyTorch models to rate-coded SNNs in one call), trainable per-synapse delays (DelayLinear with differentiable interpolation), one-command FPGA synthesis (sc-neurocore deploy model.nir --target ice40 auto-runs Yosys+nextpnr+icepack if installed; generates project files for Vivado targets), per-layer adaptive bitstream length for mixed-precision SC networks, event-driven FPGA RTL (AER encoder, event neuron, spike router — 15-39x fewer register toggles than clock-driven at 0.01-10% activity, measured), and a 6-codec neural data compression library (ISI, predictive, delta, streaming, AER) with a unified API and auto-recommendation engine — targeting BCI implants (Neuralink-scale 1024+ channels), neural probes (Neuropixels), neuromorphic inter-chip routing, and real-time closed-loop telemetry. 2 928 passing Python tests and 378 Rust tests hold 100% core coverage. 13 CI workflows guard every push. conda-forge recipe ready.

Feature Comparison

Feature	SC-NeuroCore	snnTorch	Norse	Lava	Brian2
Stochastic computing (bitstream)	Yes	—	—	—	—
Bit-true RTL co-simulation	Yes	—	—	—	—
Verilog / FPGA synthesis	Yes	—	—	Loihi only	—
IR compiler → SystemVerilog	Yes	—	—	—	—
Rust engine (39–202× vs Brian2)	Yes	—	—	—	—
Surrogate gradient training	7 surrogates, 10 cells	Yes	Yes	Yes	—
PyTorch nn.Module SNN	Yes (+ SC weight export)	Yes	Yes	—	—
GPU acceleration	PyTorch + CuPy	PyTorch	PyTorch	—	—
Neuron model library	116	11	6	3	~5 builtin
Rust neuron models (PyO3)	109	—	—	—	—
NetworkRunner (fused loop)	80 models	—	—	—	—
Network simulation engine	3 backends	PyTorch	PyTorch	Lava	C++ codegen
MPI distributed simulation	Yes	—	—	—	—
Pre-trained model zoo	10 configs, 3 weights	—	—	—	—
Spike train analysis	128 functions	—	—	—	—
Visualization plots	12	—	—	—	—
Advanced plasticity rules	13	—	—	—	—
Biological circuits	7	—	—	—	—
SC→quantum compiler	Yes	—	—	—	—
Predictive coding (SC)	Yes	—	—	—	—
Fault tolerance benchmark	Yes	—	—	—	—
Phi* (IIT) estimation	Yes	—	—	—	—
SpikeInterface adapter	Yes	—	—	—	—
NIR primitives	18/18	—	12	5	—
MNIST accuracy (SNN)	99.49%	~95%	~93%	—	—
Plasticity (STDP, R-STDP)	Yes	—	Yes	Yes	Yes
Quantum hybrid (Qiskit/PennyLane)	Yes	—	—	—	—
MLIR emitter (CIRCT)	Yes	—	—	—	—
Hyperdimensional computing	Yes	—	—	—	—
Formal verification (SymbiYosys)	7 modules, 67 props	—	—	—	—
JAX JIT training	Yes	—	—	—	—
CuPy sparse GPU	Yes	—	—	—	—
AI-optimized neurons	9 (ArcaneNeuron + 8)	—	—	—	—
Identity substrate	Yes	—	—	—	—
ANN-to-SNN conversion	Yes	—	—	—	—
Trainable per-synapse delays	Yes	—	—	—	—
One-command FPGA deploy CLI	Yes	—	—	—	—
Per-layer adaptive bitstream	Yes	—	—	—	—
Event-driven FPGA RTL (AER)	Yes	—	—	—	—
Raw waveform compression (24x)	Yes	—	—	—	—
Spike codec library (6 codecs)	Yes	—	—	—	—
Visual SNN Design Studio	Yes (web IDE)	Basic GUI	Jupyter	—	—
conda-forge recipe	Ready	Yes	—	—	Yes
PyPI package	Yes	Yes	Yes	Yes	Yes
License	AGPL-3.0	MIT	LGPL-3.0	BSD-3	CeCILL-2.1

• 125-function spike train analysis toolkit — CV, Fano factor, cross-correlation, Victor-Purpura distance, SPIKE-sync, Granger causality, GPFA, SPADE pattern detection, and 115 more functions. Matches Elephant + PySpike combined. Pure NumPy.

• Neural data compression library — Two layers: WaveformCodec compresses raw 10-bit electrode waveforms end-to-end (spike detection + template matching + LFP compression, 24x on 1024-channel Neuralink-scale data, fits Bluetooth uplink). Spike raster codecs (ISI+Huffman, Predictive with 4 learnable predictors, Delta, Streaming, AER) compress binary spike trains 50-750x. Unified API: get_codec(name), recommend_codec(). Learnable world-model predictor (99.6% accuracy). Rust backend (780x speedup). Bit-true LFSR matches Verilog RTL.

SC-NeuroCore's niche: deterministic stochastic computing with FPGA co-design — Python simulation matches synthesisable RTL bit-for-bit (deterministic LFSR seeds, Q8.8 fixed-point, cycle-exact co-simulation).

Performance: Rust Engine vs Brian2 (Brunel AI network)

Measured on i5-11600K @ 3.90 GHz, 300 ms simulation, 10% connection probability. Stored artifact: benchmarks/results/rust_scaling_benchmark.json

Scale	SC Rust	Brian2	Speedup	SC synaptic events/s
1K neurons	0.029 s	2.689 s	93×	110 M/s
5K neurons	0.285 s	4.681 s	16×	288 M/s
10K neurons	0.172 s	6.754 s	39×	1.86 B/s
50K neurons	0.582 s	31.03 s	53×	13.9 B/s
100K neurons	1.153 s	232.3 s	202×	27.7 B/s

SIMD primitives: 190 Gbit/s popcount (AVX-512 dispatch, Criterion benchmark: benchmarks/results/criterion_bitstream_2026-03-26.json)

Network Simulation Engine

Population-Projection-Network architecture with 3 backends:

Backend	Scope	Performance
Python	Any of 116 neuron models	NumPy vectorized
Rust NetworkRunner	80 models in fused Rayon-parallel loop	100K+ neurons, near-linear scaling
MPI	Billion-neuron distributed simulation via mpi4py	Multi-node HPC clusters

6 topology generators (random, small-world, scale-free, ring, grid, all-to-all), 12 visualization plots (raster, voltage, ISI, cross-correlogram, PSD, firing rate, phase portrait, population activity, instantaneous rate, spike train comparison, network graph, weight matrix), and 13 advanced plasticity rules (pair/triplet/voltage STDP, BCM, BPTT, TBPTT, EWC, e-prop, R-STDP, MAML, homeostatic, STP, structural).

Model Zoo

10 pre-built network configurations (Brunel balanced, cortical column, CPG, decision-making, working memory, visual cortex V1, auditory processing, MNIST classifier, SHD speech classifier, DVS gesture classifier) with 3 pre-trained weight sets (MNIST 784-128-10, SHD 700-256-20, DVS 256-256-11).

116 Neuron Models (1943--2026)

Every model has a uniform step(current) -> spike API, a reset(), and a cited reference. One file per model in src/sc_neurocore/neurons/models/.

Category	Count	Examples
Integrate-and-fire variants	18	AdEx, GLIF5, ExpIF, QIF, SFA, MAT, COBA-LIF, Parametric LIF, Fractional LIF
Simple spiking (2D+)	20	FitzHugh-Nagumo, Morris-Lecar, Hindmarsh-Rose, Resonate-and-Fire, Chay
Biophysical (conductance-based)	20	Hodgkin-Huxley, Connor-Stevens, Traub-Miles, Mainen-Sejnowski, Pospischil
Stochastic / population / neural mass	13	Poisson, GLM, Jansen-Rit, Wong-Wang, Wilson-Cowan, Ermentrout-Kopell
Rate / plasticity / other	12	McCulloch-Pitts (1943), Sigmoid Rate, Astrocyte, Amari, GatedLIF (2022)
Hardware chip emulators	9	Loihi CUBA, Loihi 2, TrueNorth, BrainScaleS AdEx, SpiNNaker, Akida, DPI
Multi-compartment	7	Pinsky-Rinzel, Hay L5 Pyramidal, Rall Cable, Booth-Rinzel, Dendrify
Map-based (discrete-time)	6	Chialvo, Rulkov, Ibarz-Tanaka, Cazelles, Courbage-Nekorkin, Medvedev
Core (stochastic computing)	5	StochasticLIF, FixedPointLIF, HomeostaticLIF, Dendritic, SC-Izhikevich
Training cells (PyTorch)	4	LIF, ALIF, RecurrentLIF, EProp-ALIF
AI-optimized (novel)	9	ArcaneNeuron, MultiTimescale, AttentionGated, PredictiveCoding, SelfReferential, CompositionalBinding, DifferentiableSurrogate, ContinuousAttractor, MetaPlastic

ArcaneNeuron — Self-Referential Cognition

The flagship AI-optimized model. Five coupled subsystems in a single ODE: fast compartment (tau=5ms), working memory (tau=200ms), deep context (tau=10s), learned attention gate, and a forward self-model (predictor). The deep compartment accumulates identity: it changes only on genuine novelty (prediction errors), not routine input. Confidence modulates threshold and meta-learning rate. No equivalent in any other toolkit.

Identity Substrate

Persistent spiking network for identity continuity (sc_neurocore.identity).

Module	Class	Purpose
substrate.py	IdentitySubstrate	3-population network (HH cortical + WB inhibitory + HR memory) with STDP and small-world connectivity
encoder.py	TraceEncoder	LSH-based text-to-spike-pattern encoding
decoder.py	StateDecoder	PCA + attractor extraction + priming context generation
checkpoint.py	Checkpoint	Lazarus protocol: save/restore/merge complete network state (.npz)
director.py	DirectorController	L16 cybernetic closure: monitor, diagnose, correct network dynamics

Quick Start

pip install sc-neurocore

from sc_neurocore import StochasticLIFNeuron

neuron = StochasticLIFNeuron(v_threshold=1.0, tau_mem=20.0, noise_std=0.0)
spikes = sum(neuron.step(0.8) for _ in range(500))
print(f"{spikes} spikes in 500 steps")

# Optional extras
pip install sc-neurocore[full]     # all research modules
pip install sc-neurocore[gpu]      # CuPy GPU acceleration
pip install sc-neurocore[nir]      # NIR interop (Norse, snnTorch, Lava)

pip install sc-neurocore publishes the Python suite under the public sc-neurocore package name. The optional Rust engine remains part of the repository / release-asset / source-build flow rather than a separate PyPI runtime dependency. Source-only Frontier modules such as analysis, viz, audio, dashboard, and swarm still require a source checkout.

Development Setup

git clone https://github.com/anulum/sc-neurocore.git
cd sc-neurocore
pip install -e ".[dev]"    # editable install with all dev tools
make preflight             # verify setup (lint + tests)

If you are changing the Rust bridge locally, install bridge/ in the same environment or run source-tree commands with PYTHONPATH=src:bridge.

Visual SNN Design Studio (Experimental)

Status: Development preview. The Studio is functional but under active development. API and UI may change between releases.

A web-based IDE for designing, training, compiling, and deploying spiking neural networks — from ODE equations to FPGA bitstream in a single browser tab.

pip install sc-neurocore[studio]
sc-neurocore studio              # opens browser at http://127.0.0.1:8001

Feature	What it does
118 Model Browser	Browse all neuron models by category, simulate with parameter sliders
18+ Analysis Views	Trace, phase portrait, ISI, f-I curve, bifurcation, heatmap, STA, frequency response, characterisation dashboard
Compiler Inspector	Build SC IR from equations, verify, emit SystemVerilog
Synthesis Dashboard	One-click Yosys synthesis to ice40/ECP5/Gowin/Xilinx, multi-target comparison, resource bars
Training Monitor	Live loss/accuracy curves via SSE, 6 surrogate gradients, per-layer spike rates
Network Canvas	Drag-and-drop populations and projections (React Flow), NIR export/import
Full Pipeline	Network → simulate → compile → synthesise in one click
Project Save/Load	Persistent workspaces as JSON, server-side storage

No other SNN framework provides a visual design-to-hardware pipeline. snnTorch has Jupyter notebooks. Brian2 has a basic GUI. Neither goes from visual network design to FPGA resource estimation.

Feature	SC-NeuroCore Studio	Brian2 GUI	snnTorch	Nengo GUI
Visual network design	Yes	Basic	No	Yes
ODE equation editor	Yes	No	No	No
Live training curves	Yes	No	TensorBoard	No
Verilog output viewer	Yes	No	No	No
FPGA synthesis	Yes	No	No	No
Co-simulation view	Yes	No	No	No

Full documentation: Studio Guide

Docker

The Docker image ships with the full Rust engine (39–202× faster than Brian2):

# Build
make docker-build
# or: docker build -f deploy/Dockerfile -t sc-neurocore:latest .

# Run interactive Python shell
make docker-run
# or: docker run --rm -it sc-neurocore:latest

# Smoke test via docker compose
docker compose -f deploy/docker-compose.yml up

Pre-built images are published to GHCR on every release:

docker pull ghcr.io/anulum/sc-neurocore:latest
docker run --rm -it ghcr.io/anulum/sc-neurocore:latest

Architecture

Module Tiers

pip install sc-neurocore ships Core + Simulation + Domain bridges only. Research and Frontier modules are available from source (pip install -e ".[dev]").

Tier	Modules	Ships in wheel	Status
Core	neurons, synapses, layers, sources, utils, recorders, accel, compiler, hdl_gen, hardware, cli, exceptions	Yes	Production-ready. 100% coverage.
Simulation	hdc, solvers, transformers, learning, graphs, ensembles, export, pipeline, profiling, models, math, spatial, verification, security	Yes	Stable. Import explicitly.
Domain bridges	quantum (Qiskit/PennyLane), adapters/holonomic (JAX), scpn (Petri nets)	Yes	Requires pip install sc-neurocore[quantum] or [jax]
Research	robotics, physics, bio, optics, chaos, sleep, interfaces	No	Tested. Available from source.
Frontier	generative, world_model, analysis, audio, dashboard, viz, swarm	No	Experimental. Available from source.
Speculative	research/ (eschaton, exotic, meta, post_silicon, transcendent)	No	Theoretical. See research/README.md.

Architecture Diagram

graph TD
    subgraph "Python API (pip install sc-neurocore)"
        A[BitstreamEncoder] --> B[SCDenseLayer / SCConv2DLayer]
        B --> C[116 Neuron Models<br/>LIF · HH · AdEx · Izhikevich · ArcaneNeuron · ...]
        C --> NET[Network Engine<br/>Population · Projection · 3 Backends]
        C --> ID[Identity Substrate<br/>Persistent SNN · Checkpoint · Director]
        C --> D[STDP / R-STDP Synapses]
        D --> E[BitstreamSpikeRecorder]
    end

    subgraph "Acceleration"
        B --> F{Backend?}
        F -->|CPU| G[NumPy / Numba SIMD]
        F -->|GPU| H[CuPy CUDA]
        F -->|Rust| I[sc_neurocore_engine<br/>39–202× vs Brian2 · 109 neuron models<br/>80-model NetworkRunner]
        F -->|MPI| MPI[mpi4py distributed<br/>billion-neuron scale]
    end

    subgraph "Hardware Target"
        I --> J[IR Compiler]
        J --> K[SystemVerilog Emitter]
        J --> K2[MLIR/CIRCT Emitter]
        K --> L[Verilog RTL<br/>AXI-Lite + LIF Core]
        K2 --> L
        L --> M[FPGA Bitstream<br/>Xilinx / Intel]
        L --> V[Formal Verification<br/>SymbiYosys · 7 modules]
    end

    subgraph "Domain Bridges (optional)"
        B --> N[SCPN Petri Nets]
        B --> O[Quantum Hybrid<br/>Qiskit / PennyLane]
        B --> P[HDC/VSA Symbolic Memory]
    end

    style A fill:#2d6a4f,color:#fff
    style I fill:#b5651d,color:#fff
    style L fill:#1a237e,color:#fff
    style M fill:#4a148c,color:#fff
    style O fill:#6a1b9a,color:#fff
    style V fill:#004d40,color:#fff

Core API (28 symbols)

from sc_neurocore import (
    # Neurons
    StochasticLIFNeuron, FixedPointLIFNeuron, FixedPointLFSR,
    FixedPointBitstreamEncoder, HomeostaticLIFNeuron,
    StochasticDendriticNeuron, SCIzhikevichNeuron,
    # Synapses
    BitstreamSynapse, BitstreamDotProduct,
    StochasticSTDPSynapse, RewardModulatedSTDPSynapse,
    # Layers
    SCDenseLayer, SCConv2DLayer, SCLearningLayer,
    VectorizedSCLayer, SCRecurrentLayer, MemristiveDenseLayer,
    SCFusionLayer, StochasticAttention,
    # Utilities
    BitstreamEncoder, BitstreamAverager, RNG,
    generate_bernoulli_bitstream, generate_sobol_bitstream,
    bitstream_to_probability,
    # Sources & Recorders
    BitstreamCurrentSource, BitstreamSpikeRecorder,
)

Hardware (Verilog RTL)

hdl/
  sc_bitstream_encoder.v      -- LFSR-based stochastic encoder (SEED_INIT param)
  sc_bitstream_synapse.v      -- AND-gate SC multiplier
  sc_mux_add.v                -- 2-input MUX (scaled addition)
  sc_cordiv.v                 -- CORDIV stochastic divider (Li et al. 2014)
  sc_dotproduct_to_current.v  -- Popcount -> fixed-point current
  sc_lif_neuron.v             -- Q8.8 leaky integrate-and-fire
  sc_firing_rate_bank.v       -- Spike rate estimator
  sc_dense_layer_core.v       -- Full dense layer pipeline (decorrelated seeds)
  sc_dense_matrix_layer.v     -- N×M weight matrix layer
  sc_axil_cfg.v               -- AXI-Lite register file
  sc_axil_cfg_param.v         -- Parameterized AXI-Lite register file
  sc_axis_interface.v         -- AXI-Stream bulk bitstream I/O
  sc_dma_controller.v         -- DMA for weight upload and output readback
  sc_cdc_primitives.v         -- Clock domain crossing (2-FF sync, Gray, async FIFO)
  sc_dense_layer_top.v        -- Dense layer top wrapper
  sc_neurocore_top.v          -- System top (DMA + AXI + layers)
  sc_aer_encoder.v            -- AER spike encoder (event-driven output)
  sc_event_neuron.v           -- Event-triggered LIF (power ∝ spike rate)
  sc_aer_router.v             -- AER event distribution to target neurons
  tb_sc_*.v (7 testbenches)   -- Self-checking simulation testbenches
  formal/ (7 modules)         -- SymbiYosys formal verification properties

GPU Acceleration

from sc_neurocore.accel import xp, HAS_CUPY, to_device, to_host
from sc_neurocore.accel.gpu_backend import gpu_vec_mac

# VectorizedSCLayer auto-detects GPU
layer = VectorizedSCLayer(n_inputs=32, n_neurons=64, length=1024)
output = layer.forward(input_values)  # GPU if CuPy available, else CPU

Hardware-Software Co-Simulation

The co-sim flow verifies bit-exact equivalence between the Python model and Verilog RTL:

# 1. Generate stimuli + expected results (Python golden model)
python scripts/cosim_gen_and_check.py --generate

# 2. Run Verilog simulation (requires Icarus Verilog)
iverilog -o tb_lif hdl/sc_lif_neuron.v hdl/tb_sc_lif_neuron.v
vvp tb_lif

# 3. Compare results
python scripts/cosim_gen_and_check.py --check

Reproducibility

Every GitHub Release includes:

• wheel + sdist — Python distribution artifacts (dist/sc_neurocore-*)
• SBOM — CycloneDX software bill of materials (sbom.json)
• Changelog extract — release notes from CHANGELOG.md

Co-simulation traces are generated deterministically from fixed LFSR seeds. To reproduce a published benchmark:

git checkout v3.13.3
pip install -e ".[dev]"
python benchmarks/benchmark_suite.py --markdown > BENCHMARKS.md

For Verilog co-sim trace reproduction, see scripts/cosim_gen_and_check.py and the seed constants in hdl/sc_bitstream_encoder.v.

Key Technical Details

• LFSR: 16-bit maximal-length, polynomial x^16+x^14+x^13+x^11+1, period 65535
• Seed strategy: Input encoders 0xACE1 + i*7, weight encoders 0xBEEF + i*13
• Fixed-point: Q8.8 (DATA_WIDTH=16, FRACTION=8), signed two's complement
• Overflow: Explicit bit-width masking via _mask() function

Examples

Runnable scripts in examples/:

Script	Description
01_basic_sc_encoding.py	Bernoulli & Sobol bitstream encoding/decoding
02_sc_neuron_layer.py	SCDenseLayer construction, spike trains, and firing-rate summary
03_ir_compile_demo.py	IR graph building, verification, SystemVerilog emission (v3 Rust engine)
04_vectorized_layer.py	VectorizedSCLayer throughput benchmarking
05_scpn_stack.py	Full 7-layer SCPN consciousness stack with inter-layer coupling
06_hdl_generation.py	Verilog top-level generation from a network description
07_ensemble_consensus.py	Multi-agent ensemble orchestration and voting
08_hdc_symbolic_query.py	Hyper-Dimensional Computing symbolic memory (v3 Rust engine)
09_safety_critical_logic.py	Fault-tolerant Boolean logic with stochastic redundancy (v3 Rust engine)
10_benchmark_report.py	Head-to-head v2/v3 benchmark suite (v3 Rust engine)
11_sc_training_demo.py	Surrogate-gradient training of an SC dense layer (v3 Rust engine)
12_load_pretrained_model.py	Load pretrained ConvSpikingNet and classify MNIST digits
jax_training_demo.py	JAX JIT surrogate-gradient SNN training on synthetic data
mnist_fpga/demo.py	MNIST classifier: train → quantise Q8.8 → SC simulate → Verilog export
mnist_conv_train.py	ConvSpikingNet: 99.49% MNIST (learnable beta/threshold, cosine LR)
mnist_surrogate/train.py	Surrogate gradient SNN training (FastSigmoid/SuperSpike/ATan, ~95% MNIST)
nir_roundtrip_demo.py	NIR roundtrip: CubaLIF + recurrent connections, build → import → run → export
norse_nir_roundtrip.py	Norse → NIR → SC-NeuroCore roundtrip with real Norse weights
snntorch_nir_roundtrip.py	snnTorch RSynaptic → NIR → SC-NeuroCore roundtrip (CubaLIF + recurrent)
spikingjelly_nir_roundtrip.py	SpikingJelly → NIR → SC-NeuroCore roundtrip
ann_to_snn_demo.py	Convert trained PyTorch ANN to rate-coded SNN
delay_training_demo.py	Train spiking network with learnable per-synapse delays

PYTHONPATH=src:bridge python examples/01_basic_sc_encoding.py

Examples marked (v3 Rust engine) require an available sc_neurocore_engine bridge install. For source-tree runs against local bridge code, use PYTHONPATH=src:bridge or install bridge/ in the same environment.

CI/CD

13 GitHub Actions workflows (.github/workflows/), all SHA-pinned:

Workflow	Purpose
ci.yml	Lint (ruff format + ruff check + bandit) + Test (Python 3.10-3.14, coverage = 100%) + Build
v3-engine.yml	Rust engine cargo test + cargo clippy
v3-wheels.yml	Cross-platform wheels (Linux, macOS, Windows × Python 3.10–3.14)
docker.yml	Build & push Docker image to GHCR on release tags
docs.yml	MkDocs → GitHub Pages
publish.yml	Publish sc-neurocore to PyPI and engine/ to crates.io on release tags
release.yml	Python wheel + sdist + changelog extraction → GitHub Release
benchmark.yml	Performance regression tracking
codeql.yml	CodeQL security analysis (weekly + on push)
scorecard.yml	OpenSSF Scorecard
pre-commit.yml	Pre-commit hook validation
yosys-synth.yml	Yosys HDL synthesis verification
stale.yml	Auto-label and close stale issues

Benchmarks

Run the benchmark suite:

python benchmarks/benchmark_suite.py           # quick mode
python benchmarks/benchmark_suite.py --full    # thorough (10x)
python benchmarks/benchmark_suite.py --markdown # output BENCHMARKS.md

Sample results (CPU, quick mode):

Operation	Throughput
LFSR step	2.25 Mstep/s
Bitstream encoder	1.88 Mstep/s
LIF neuron step	1.15 Mstep/s
vec_and (1024 words)	45.67 Gbit/s
gpu_vec_mac (64x32x16w)	6.15 GOP/s

Documentation

Live site: anulum.github.io/sc-neurocore

• Getting Started — Installation & quickstart
• Tutorials — 51 hands-on guides (SC fundamentals → MNIST → FPGA → quantum → formal verification)
• API Reference — Python package API
• Rust Engine API — Rust engine docs
• Hardware Guide — FPGA deployment workflow
• Architecture — Package architecture
• Benchmarks — Performance measurements
• CHANGELOG.md — Version history

Build docs locally:

pip install mkdocs mkdocs-material mkdocstrings[python]
mkdocs serve

Install Extras

pip install sc-neurocore              # core engine only (neurons, layers, compiler, HDL gen)
pip install sc-neurocore[accel]       # + Numba JIT acceleration
pip install sc-neurocore[gpu]         # + CuPy CUDA acceleration
pip install sc-neurocore[jax]         # + JAX backend for holonomic adapters
pip install sc-neurocore[quantum]     # + Qiskit + PennyLane quantum bridges
pip install sc-neurocore[lava]        # + Intel Lava interop (Loihi target)
pip install sc-neurocore[research]    # + matplotlib, networkx, onnx, torch
pip install sc-neurocore[full]        # + numba, matplotlib, networkx, onnx, qiskit, pennylane

For development (includes all modules + research/frontier code from source):

pip install -e ".[dev]"               # editable install with pytest, mypy, ruff, hypothesis

Pinned dependency files for reproducible environments:

pip install -r requirements.txt       # runtime only
pip install -r requirements-dev.txt   # runtime + dev tools

Rust Engine (109 Neuron Models, 378 Tests)

The sc_neurocore_engine crate provides 109 Rust neuron models callable from Python via PyO3 bindings (including ArcaneNeuron), an 80-model NetworkRunner with Rayon-parallel population simulation (100K+ neurons), and SIMD-accelerated primitives with dispatch across five ISAs (AVX-512, AVX2, NEON, SVE, RISC-V V).

378 Rust tests across 17 test binaries.

Category	Scope
Primitives	Bernoulli + Sobol bitstream, pack/unpack, popcount, SIMD (5 ISAs)
Neurons	109 models: LIF variants, HH-type, maps, hardware emulators, population, ArcaneNeuron
NetworkRunner	80-model fused simulation loop with CSR projections and Rayon parallelism
Synapses	Static, STDP, Reward-STDP
Layers	Dense, Conv2D, Recurrent, Learning, Fusion, Memristive, Attention
Networks	Brunel, GNN, Spike recorder, Connectome, Fault injection
Compiler	IR builder/parser/verifier, SystemVerilog + MLIR emitters, IR bridge
Domain	HDC, Kuramoto, SSGF geometry
Training	6 surrogate gradient functions + property tests

Community

• GitHub Discussions — questions, ideas, show & tell
• Issue Tracker — bug reports and feature requests
• Contributing Guide — how to set up, test, and submit PRs

Citation

If you use SC-NeuroCore in your research, please cite:

@software{sotek2026scneurocore,
  author    = {Šotek, Miroslav},
  title     = {SC-NeuroCore: A Deterministic Stochastic Computing Framework for Neuromorphic Hardware Design},
  version   = {3.13.3},
  year      = {2026},
  doi       = {10.5281/zenodo.18906614},
  url       = {https://github.com/anulum/sc-neurocore},
  license   = {AGPL-3.0-or-later}
}

See also CITATION.cff for the machine-readable citation metadata.

AI Disclosure

This project uses LLMs for advanced control mechanisms and GitHub handling. All output is reviewed, tested, and verified by the project author.

License

SC-NeuroCore is dual-licensed:

• Open Source: GNU Affero General Public License v3.0 (AGPLv3)
• Commercial: Proprietary license available for integration into closed-source products

For commercial licensing enquiries, contact [e-mail].

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Developed by ANULUM / Fortis Studio