SC-NeuroCore

June 15, 2026 · View on GitHub

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

SC-NeuroCore

SC-NeuroCore — Stochastic Computing & Neuromorphic Engine

Stochastic computing and neuromorphic hardware co-design toolkit

Version: 3.15.34

SC-NeuroCore is a research-to-hardware software stack for designing spiking and stochastic neural systems, validating their numerical behaviour, and moving selected models toward FPGA, ASIC, and embedded neuromorphic deployment. It combines a Python public API, an optional Rust acceleration engine, SystemVerilog generation paths, benchmark evidence, and polyglot research mirrors for selected kernels.

It is useful when a team needs to answer practical questions such as:

Can a stochastic bitstream representation approximate this neural computation within a bounded error budget?
Which neuron, synapse, encoder, or network path is numerically appropriate for a target workload?
Can a model be converted into auditable hardware artefacts rather than remaining only a Python simulation?
Which evidence is available for speed, parity, synthesis, safety-case readiness, or cross-framework comparison?
Which capabilities are stable package surfaces and which remain source-checkout research surfaces?

LIF spike raster — 5 neurons, sinusoidal input

Who It Is For

Audience	Primary value
Neuroscience and SNN researchers	Reproducible neuron and network experiments with explicit numerical guardrails.
Hardware and FPGA engineers	Bitstream arithmetic, fixed-point export, RTL generation, and synthesis evidence workflows.
ML and edge-computing teams	SNN training, ANN-to-SNN conversion, acceleration options, and deployment trade-off studies.
Safety, verification, and industrial teams	Evidence bags, fail-closed readiness checks, formal-verification collateral, and documentation boundaries.
Commercial evaluators	A mapped path from modelling to hardware-oriented evidence, with clear gaps before field deployment.

What Makes It Distinct

Stochastic computing first: bitstream encoders, probabilistic arithmetic, Sobol/LFSR sources, and bounded error reasoning are first-class components.
Neuromorphic breadth with guardrails: the repository contains a wide neuron/model catalogue, but public docs separate stable package surfaces from opt-in research paths.
Hardware-oriented output: selected workflows generate Verilog/SystemVerilog artefacts, synthesis reports, and hardware-readiness evidence instead of stopping at simulation.
Evidence-indexed benchmarking: public benchmark claims are tied to committed artefacts under benchmarks/results/ or hardware reports under hdl/reports/.
Polyglot parity research: Julia, Go, Mojo, Rust, and Python counterparts exist for selected kernels to test implementation fidelity and performance trade-offs, while the default install remains Python-first.

Application Areas

SC-NeuroCore is positioned for neuromorphic R&D, stochastic accelerator design, edge inference studies, BCI and spike-codec prototyping, safety-case tooling, and hardware/software co-design. It is not a certified medical, automotive, aerospace, or rail product by itself. Domain profiles in the documentation state required evidence and missing evidence explicitly so commercial or regulated deployment work can start from an auditable gap list.

Current Evidence Boundary

Surface	Current status
Public Python package	Version 3.15.34 package surface with base NumPy/SciPy dependency boundary.
Optional acceleration	Rust engine and optional heavy backends are opt-in; Python fallbacks remain available.
Hardware evidence	Committed synthesis and report artefacts exist for selected flows; power/energy claims require matching committed reports.
Benchmarks	Only committed JSON/CSV/report artefacts are public evidence. Local exploratory runs must not be promoted without raw artefacts.
Polyglot surfaces	Source-checkout research and parity surfaces, not default user install requirements.
Regulated deployment	Readiness tooling and evidence categories only; no certification or field approval claim.

Version and Capability Snapshot

SC-NeuroCore Capability Inventory

Surface	Current inventory
Package version	3.15.34
Public API exports	44
Python model source modules	151
Python model classes	157
Model documentation pages	174
Rust PyO3 model wrappers	175
Optional extras	24
Python test files	788
Public documentation pages	545
GitHub Actions workflows	14

Evidence boundary: this snapshot is a static inventory. Performance, coverage, hardware, and scientific-fidelity claims require their own committed evidence artefacts.

Quick Start

pip install sc-neurocore

For biological closed-loop BCI implementations (experimental), install the bioware optional dependencies:

pip install "sc-neurocore[bioware]"

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")

Zenith Quickstart

Train biologically plausible rules using PyTorch surrogate autograd, then export the exact layer to hardware:

Bio-hybrid Closed-Loop with ArcaneZenith (Experimental)

The bioware module securely bridges biological real-world MEA setups into Stochastic Computing and Optogenetic laser outputs natively. It supports PCA/K-Means spike sorting, runtime health tracking, pharmacological wash simulations, and ArcaneZenithCognitiveCore bridged bindings.

import torch
from sc_neurocore.plasticity import create_plasticity_layer
from sc_neurocore._native.learning_bridge import RULE_STDP

# 1. Train entirely in standard DL execution architectures
bcm_layer = create_plasticity_layer(count=128, rule_type=RULE_STDP, backend="torch", autograd=True)
# ... standard cross-entropy loss.backward() loop

# 2. Deploy natively to SC-NeuroCore hardware limits
exascale_layer = create_plasticity_layer(count=128, rule_type=RULE_STDP, backend="rust", weight=bcm_layer.weights.detach().numpy())
exascale_layer.save("hw_layer.scal")

See the full end-to-end integration demo in examples/zenith_hybrid_resnet.py.

# Add only the extras needed for the current workflow.
pip install "sc-neurocore[core]"      # explicit base profile
pip install "sc-neurocore[nir]"       # NIR interop
pip install "sc-neurocore[training]"  # PyTorch-backed training
pip install "sc-neurocore[studio]"    # local web studio
pip install "sc-neurocore[full]"      # local research environment only

See Install Profiles for the full optional dependency matrix and research-only boundaries.

Rust Engine (39–202× faster)

The optional Rust engine provides SIMD-accelerated simulation, 174 neuron models via PyO3, and fused E-I network simulation. Pre-built wheels are available through repository release assets or source builds when present in the local environment.

When installed, SC-NeuroCore automatically uses the Rust engine for:

NetworkRunner: 161-model fused Rayon-parallel simulation loop
E-I network: single Rust call for connectivity + Poisson + Euler + spike detection
Batch simulate: model dispatch loop in compiled Rust
SIMD bitstream ops: 190 Gbit/s popcount (AVX-512)

The pure Python package works without the engine — NumPy fallbacks are used for all operations. Install or build the engine only when you need the performance advantage. See Install Profiles for the base install, optional extras, and source-build path.

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 extended 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 until the v4.0 stable API freeze.

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 .

# Build the offline HDL shipping profile with packaged baseline RTL primitives
# and the hash-locked HDL dependency set. Vivado is not required for this image.
docker build -f deploy/Dockerfile --build-arg INSTALL_EXTRAS=hdl -t sc-neurocore:hdl .

# 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 extended 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 path; current CI coverage gate is 96%, with 100% retained as the target.
Simulation	hdc, solvers, transformers, learning, graphs, ensembles, export, pipeline, profiling, models, math, spatial, verification, security	Yes	Stable. Import explicitly.
Industrial	safety_cert, asic_flow, fault_injection, uvm_gen, hypervisor, digital_twin, chiplet, spintronic, memristor, analog_bridge	No	1,173 tests. Available from source.
Extended research	evo_substrate, meta_plasticity, bioware, federated, bci_studio, explainability, neuro_symbolic, stochastic_doctor, model_zoo	No	1,173 tests. Available from source.
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.
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[173 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 · 174 neuron models<br/>161-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
`zenith_hybrid_resnet.py`	Train hybrid network with PyTorch autograd → save via Zenith exascale persistence
`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; evidence in `benchmarks/results/mnist_conv_accuracy_reproducibility.json`)
`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

14 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 gate enforced in CI) + 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
Install Profiles — Base install, optional extras, and research-only polyglot boundary
FPGA Deploy Cookbook — Five-minute scaffold, optional synthesis, report-to-optimiser handoff
Tutorials — 88 tracked guides and tutorials (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

Start with the base package. It installs the Python package plus numpy and scipy; it does not install PyTorch, JAX, Qiskit, PennyLane, Lava, FastAPI, or hardware toolchains.

pip install sc-neurocore              # base package: core simulation, compiler, HDL scaffold
pip install sc-neurocore[core]        # explicit base profile
pip install sc-neurocore[training]    # PyTorch-backed training
pip install sc-neurocore[nir]         # NIR import/export
pip install sc-neurocore[studio]      # local web studio
pip install sc-neurocore[bioware]     # biological closed-loop prototypes

Acceleration and research extras are intentionally opt-in:

pip install sc-neurocore[accel]       # Numba JIT experiments
pip install sc-neurocore[gpu]         # CuPy CUDA experiments
pip install sc-neurocore[jax]         # JAX-backed experiments
pip install sc-neurocore[quantum]     # research-grade Qiskit/PennyLane bridges
pip install sc-neurocore[lava]        # Lava interop experiments
pip install sc-neurocore[research]    # plotting, graph, ONNX, and torch research stack
pip install sc-neurocore[full]        # local research environment only; pulls heavy extras

See Install Profiles before using full. The default package and FPGA scaffold flow do not require those heavy extras.

For development (includes all modules and source-only research code):

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 (174 Neuron Models, 1 720 Tests across 6 Crates)

The sc_neurocore_engine crate provides 174 Rust neuron models callable from Python via PyO3 bindings (including ArcaneNeuron), a 161-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).

1 720 Rust tests across 6 workspace crates:

Crate	Tests	Purpose
`sc_neurocore_engine`	1,552	PyO3 SIMD engine, 174 neuron models, NetworkRunner
`tinysc_riscv`	83	RISC-V SC instruction set simulator
`core_engine`	22	SC arithmetic core (standalone)
`autonomous_learning`	12	Self-modifying plasticity rules
`neuro_symbolic`	28	Hyperdimensional computing + predictive coding
`stochastic_doctor_core`	23	Bitstream diagnostics engine

Category	Scope
Primitives	Bernoulli + Sobol bitstream, pack/unpack, popcount, SIMD (5 ISAs)
Neurons	174 models: LIF variants, HH-type, maps, hardware emulators, population, ArcaneNeuron
NetworkRunner	161-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   = {3.15.34},
  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 protoscience@anulum.li.

Developed by ANULUM / Fortis Studio