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Announcing Jneopallium: Biologically-Grounded Neuron Networks with Industrial-Grade Safety for Real Plants

Hugging Face Forums [Unofficial] May 8, 2026
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Hi Hugging Face community!Today I’m excited to share Jneopallium — a Java framework for building biologically-grounded, multi-timescale neuron networks that can actually run as safety-gated controllers in chemical plants, power grids, manufacturing lines, water treatment, and HVAC systems.While most of the ecosystem focuses on Python/PyTorch models for the cloud or edge inference, Jneopallium was designed from the ground up for industrial operational technology (OT) — where a single bad write to an actuator can have serious consequences.What makes it different

  • True biological grounding: Typed signals with independent propagation, receptor heterogeneity, dual fast/slow processing loops (regulatory vs supervisory timescales), and per-signal frequency control via ProcessingFrequency(loop, epoch). It’s not just “bio-inspired” — the architecture mirrors how natural neuron networks actually work.

  • Industrial OPC UA bridge (Eclipse Milo-based): Full read/write integration with any standards-compliant PLC, SCADA, or digital twin. Subscriptions, alarms & conditions, quality propagation, wall-clock timestamps from the server — all handled cleanly.

  • Six non-negotiable safety invariants baked into the bridge:

    1. No raw actuator writes — everything goes through Planning → SafetyGate → Interlock → OperatorOverride → Aggregator.

    2. Interlocks have absolute authority (fail-safe write on trip).

    3. Operator override always wins for regulatory control.

    4. Every single write is audited (JSONL + optional OPC UA mirror).

    5. Quality never silently promoted.

    6. Timestamps come from the plant, not the JVM.

  • Progressive autonomy done right: Per-loop SHADOW → ADVISORY → AUTONOMOUS commissioning sequence. You can run 90 % of loops fully autonomous while keeping 1 % in full shadow mode. No dangerous global “AI on/off” switch.

  • Built-in safety neurons: SafetyGateNeuron, InterlockNeuron, Human-harm discriminator (five-dimensional consequence model), OscillationMonitor, etc.

License: BSD 3-Clause (fully open).Quick start (industrial smoke test)

yaml

# Minimal unsecured demo config (public Milo server)
connection:
  endpointUrl: "opc.tcp://milo.digitalpetri.com:62541/milo"
...

Add the single Maven dependency, drop in the YAML, run the bootstrap class, and you’re connected. Full manual (with worked temperature-loop example, cascaded loops, audit format, etc.) is included in the repo.Links

Jneopallium is still early (1.0-SNAPSHOT) but already battle-tested in concept for real industrial use. The core framework is mature; the industrial module is production-ready for controlled commissioning.I’d love feedback from the HF community — especially from people working on:

  • Safe autonomous agents

  • Industrial / robotics control

  • Multi-timescale or biologically-plausible architectures

  • OT cybersecurity & functional safety

If you’re in process control, functional safety, or just curious about neuron nets that can actually touch physical hardware without blowing up the plant — come say hi!Star the repo if this resonates, try the demo, and let me know what you think.— Dmytro Rakovskyi

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