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EvoSpikeNet Project Feature Implementation Status

  • Author: Masahiro Aoki
  • Copyright: 2026 Moonlight Technologies Inc. All Rights Reserved.

Implementation note: See tools/implementation_manifest.md for artifact_manifest.json produced by training scripts and recommended CLI flags.

Last updated: 2026-08-15 Organization policy: This document reorganizes all items in Remaining_Functionality into four categories based on source-code verification: 1. Update history 2. Detailed unimplemented items 3. Detailed in-progress items 4. Implemented items

Status determination criteria - Implemented: The implementation exists in the repository and is usable in at least unit, integration, or E2E tests. - In progress: Placeholder, partial implementation, staged migration, integration pending, or optimization in progress. - Unimplemented: No repository implementation exists, or completion depends on external operational/contractual procedures.

Primary reference documents - docs-dev/connectome_evospikenet_implementation_policy.ja.md - docs-dev/connectome_schema.md - docs/BIOMIMETIC_IMPLIMENTATION_PLAN.md - docs/DISTRIBUTED_BRAIN_SPATIAL_NODES.md - docs/SDK_API_REFERENCE.md

2026-04-24 operational hardening update

  • evospikenet.api no longer pretends to support a limited startup mode without PyTorch. It now fails fast with an explicit startup error.
  • Video-analysis production behavior can be forced fail-closed with VIDEO_ANALYSIS_FAIL_CLOSED=true, and degraded responses now carry source_backend, fallback_reason, quality_level, and degraded.
  • AvailabilityMonitor no longer starts a monitoring thread on module import. Model inference checks require an explicit model_health_check callback or model.health_check() implementation.
  • Distributed-brain strict mode (EVOSPIKENET_STRICT_FUNCTIONAL_MODULES=true) no longer backfills missing genome / chromosome / topology with default modules.

1. Update History

This chapter records major updates in phase order.

1.1 Phase A/B — Biomimetic integration completed (2026-03-06)

Resolved integration gaps between evospikenet/biomimetic/ and brain_simulation.py, completing the mandatory Phase A items and recommended Phase B items.

Phase Item Implementation file
A-1 Export symbol cleanup for biomimetic/__init__.py evospikenet/biomimetic/__init__.py
A-2 Add BrainSimulationFramework evospikenet/brain_simulation.py
A-3 STDP.with_neuromodulation() and connect_plasticity_gate() evospikenet/plasticity.py
A-4 Full implementation of SleepConsolidation.offline_consolidation() evospikenet/biomimetic/sleep_consolidation.py
B-1 Neuromodulator registry bridge evospikenet/biomimetic/neuromodulators.py
B-2 Izhikevich backend integration evospikenet/brain_simulation.py
B-3 Small-world cortical topology integration evospikenet/brain_simulation.py
B-4 Delegate ERB-scale gammatone implementation evospikenet/biomimetic/sensory_motor.py
B-5 Adaptive gain for EfferenceCopy evospikenet/biomimetic/motor_efference.py
B-6 MirrorNeuronSystem._default_classify() evospikenet/biomimetic/mirror_neurons.py
B-7 run_idle_phase() DMN idle cycle evospikenet/brain_simulation.py

1.2 Phase C — Biomimetic integration completed (2026-03-09)

Completed Phase C items; 130 tests passed.

Phase Item Implementation file
C-1 HippocampalBuffer.transfer_to_semantic() evospikenet/biomimetic/hippocampal_memory.py
C-2 Expand SleepWakeCycleController evospikenet/biomimetic/sleep_wake.py
C-3 Neuromodulator state REST / Zenoh endpoints evospikenet/api_modules/biomimetic_api.py
C-4 Full integration of cortical topology and HRDA evospikenet/brain_architecture.py

1.3 Phase D — Distributed node integration completed (2026-03-11)

Resolved integration between the evolution engine and distributed brain nodes.

Phase Item Implementation file
D-1 BrainSimulation backward-compatible alias evospikenet/brain_simulation.py
D-2 InstantiatedBrain.apply_weight_delta() evospikenet/genome_to_brain.py
D-3 DistributedBrainNode.deploy_genome() evospikenet/distributed_brain_node.py
D-4 DistributedEvolutionEngine.deploy_to_nodes() evospikenet/distributed_evolution_engine.py

1.4 Phase E-0 — Connectome integration design completed (2026-03-18)

Added design assets, schemas, configuration files, and test scaffolding.

Artifact Contents
docs-dev/connectome_evospikenet_implementation_policy.ja.md Implementation policy (full)
docs-dev/connectome_schema.md JSON / NPZ schema
config/connectome_config.yaml CAVE API, cache and constraint settings
tests/test_connectome_loader.py Loader, reduction and cache performance tests
tests/test_lif_structural_mask.py Structural mask tests
tests/test_sync_connectome_integration.py Sync, rollback and 429 handling tests

1.5 Phase E-1 / E-2 — Connectome integration implementation completed (2026-03-19)

Phase Item Implementation file
E-1-1 New connectome_loader.py evospikenet/connectome_loader.py
E-1-2 ConnectomeLIFLayer extension evospikenet/core.py
E-1-3 C. elegans 302 neuron PoC data/connectome/celegans_cook2019_c_elegans_302n.json
E-1-4 FlyWire visual subgraph injection evospikenet/core.py
E-1-5 E/I ratio regression tests tests/test_connectome_loader.py, tests/test_lif_structural_mask.py
E-2-1 Node mapping and manifest construction evospikenet/connectome/node_mapping.py
E-2-2 SparseDelayBuffer implementation evospikenet/connectome/delay_buffer.py
E-2-3 ConnectomeMetadataPublisher evospikenet/zenoh_connectome_publisher.py
E-2-4 Production connectome tuning values config/connectome_config.yaml
E-2-5 ETag / TTL cache diff detection evospikenet/connectome_loader.py

1.6 Phase E-3 — Production sync features completed (2026-03-19)

Internal Phase E-3 tasks are complete. External procedural item E-3-5 has been moved to the Unimplemented section.

Phase Item Implementation file
E-3-1 Connectome auto-sync pipeline scripts/sync_connectome.py
E-3-2 EvoGenome ↔ structural_mask constraint linkage evospikenet/evolution_engine.py
E-3-3 HCP routing delay optimization evospikenet/brain_routing.py
E-3-4 Whole-brain E2E validation tests/e2e/test_connectome_e2e.py

1.7 Misc peripheral updates (2026-03-05)

  • Added Rayleigh test and wPLI zero-division guard in evospikenet/eeg_integration/comparative_analysis.py.
  • Fixed EEG pipeline compatibility in eeg_translator.py, spectrum_converter.py, and device_interface.py.
  • Clarified HTTP 403 handling for OPA security denials.

1.8 Video analysis/EEG/ROS2 updates (2026-05-06)

  • Video analysis: prod/staging default fail-closed and backend startup checks for real backends.
  • EEG: added gRPC streaming definitions (eeg_streaming_pb2.py, eeg_streaming_pb2_grpc.py).
  • Embodied PLA: ROS2-backed _ROSRobotInterface implementation reflected.

1.9 Operations templates and readiness checks (2026-05-07)

  • Video analysis: added operational templates and strict-mode readiness diagnostics.
  • EEG PTP: added hardware clock deployment template and precision/health checks.
  • mTLS: added proxy header forwarding template and diagnostics helper.
  • Alerting: added Slack/SMTP/PagerDuty configuration templates and status checks.

1.9 Document history

Date Version Changes
2026-02-02 v1.0 Reorganized features and created Remaining_Functionality.md
2026-02-25 v1.1 Reflected biomimetic items 11-1..11-19 and final implementation plans
2026-03-18 v1.2 Added Section 17 and connectome design assets
2026-03-19 v1.3 Reflected Phase E-0 / E-1 / E-2 completions
2026-03-19 v1.4 Reflected internal Phase E-3 completions
2026-04-02 v1.5 Reorganized all items into Update History / Unimplemented / In-Progress / Implemented categories
2026-04-19 v1.6 Synced 2026-04-18/19 source revalidation updates, including video-analysis operational hardening and RAG Celery/Redis implementation status
2026-04-21 v1.7 Implemented the detailed plan in 2.4.3.a: production-grade API path for video-analysis worker flow, placement/reconfiguration with scenario constraints and snapshot/restore, and ethics explanation/safety audit logging with schema + runbook updates.
2026-05-06 v1.8 Reflected video-analysis strict backend updates, EEG gRPC definitions, and ROS2 interface implementation.
2026-05-07 v1.9 Added ops templates and readiness diagnostics for video analysis, EEG PTP, mTLS proxies, and alert channels.
2026-05-16 v2.0 Added DevicePlugin quantum extension (IBM/QAOA), SDK device-plugin template, and synchronized related docs.
2026-05-16 v2.1 Added IBM Runtime Sampler/Estimator paths, QAOA optimization loop, and secret-gated live CI connectivity job.
2026-05-17 v2.2 Added OptimizationPlugin framework, Quantization/Pruning/Fusion plugins, and YAML pipeline support to complete Phase 6.
2026-05-17 v2.3 Added Brain2Loihi simulator path in Loihi DevicePlugin with SDK sample, guide, and unit tests.
2026-05-17 v2.4 Added IBM NeuroChip DevicePlugin support with SDK sample, guide updates, and unit tests.
2026-05-17 v2.5 Enumized IBM NeuroChip NorthPole runtime error codes, added NorthPole mock E2E CI smoke coverage, and added AIST G-QuAT DevicePlugin support with SDK/CI/test paths.
2026-06-08 v2.6 Synced public docs with env-backed API key refresh, tokenizer env override, RAG duplicate suppression, and EpisodicMemory capacity controls.
2026-08-02 v2.7 Revalidated the text-only Sparse Event-Memory LM boundary and added the MEMERIED SpikeNetLM plan for audio, vision, multimodal integration, SDK/artifact contracts, and acceptance criteria.
2026-08-03 v2.8 Added the artifact-manifest, continued-training, and inference compatibility contract required for application-configurable d_model values and coexisting LanguageModels.
2026-08-03 v2.9 Added Mineral LanguageMode GPU capacity queries and launch preflight, documented Core's unchanged-submitted-configuration contract, visible-GPU selection, H100 portability, and operational limits.
2026-08-14 v2.10 Reflected the Sparse Evo-MemoryLM review conclusion by adding the integrated memory-learning control design and fail-fast implementation plan.
2026-08-14 v2.11 Added /api/memory/orchestrate with runtime model selection (stub/api_loaded/evolm_backend), fail-fast error-envelope mapping, and audit-event consistency tests.
2026-08-14 v2.12 Revalidated the implemented orchestration contract, confirming the runtime-model API, fail-fast flow, and audit/event regression tests pass together.
2026-08-23 v2.13 Added end-to-end propagation of tenant_id / user_id / client_id, anonymous compatibility via public normalization, strict-mode enforcement via EVOSPIKENET_IDENTITY_STRICT=true, tenant-scoped retrieval/writeback, and validated this with 21 passing tests.

1.10 Multi-tenant identity isolation implementation (2026-08-23)

  • Implemented:
  • Propagated tenant_id, user_id, and client_id through MemoryOrchestrator.process_request() into retrieval, learning, writeback, and fallback persistence.
  • Added anonymous compatibility by defaulting missing tenant_id to public; user_id and client_id remain optional unless strict mode is enabled.
  • Added strict API validation in evospikenet/api_modules/memory_api.py: when EVOSPIKENET_IDENTITY_STRICT=true, the request is rejected with 400 if required identity fields are missing.
  • Enforced tenant boundaries in memory retrieval/writeback so cross-tenant mixing is prevented.
  • Stored persistent fallback snapshots under <persistent_fallback_dir>/<tenant_id>/.
  • Verification:
  • pytest -q tests/unit/test_causal_and_resilience.py evospikenet/tests/test_memory_orchestrator.py evospikenet/tests/test_memory_api_orchestrate.py
  • Result: 21 passed.

1.11 DevicePlugin and quantum-layer integration update (2026-05-16)

  • Implemented:
    • IBMQuantumPlugin with non-qiskit fallback behavior.
    • QAOANeuronLayer and QAOANeuronLayerPlugin.
    • SDK sample template: examples/sdk/programs/device_plugin_template.py.
  • Documentation:
    • Added SDK_DEVICE_PLUGIN_GUIDE.en.md with template and implementation steps.
  • Test:
    • Added tests/unit/test_ibm_quantum_plugin.py to guarantee baseline behavior even without optional deps.

1.11 IBM Runtime and QAOA execution update (2026-05-16)

  • Implemented:
    • Added staged QiskitRuntimeService connectivity to IBMQuantumPlugin.
    • Added run_sampler() / run_estimator() execution paths.
    • Added counts/probabilities reconstruction and a lightweight optimization loop to QAOANeuronLayer.
    • Added SDK sample examples/sdk/programs/ibm_quantum_runtime_demo.py.
  • CI:
    • Added ibm_quantum_runtime profile to the optional dependency matrix.
    • Added a live connectivity job that only runs when QISKIT_IBM_TOKEN is available.
  • Test:
    • Added mocked runtime connectivity, Sampler/Estimator, and optimized-parameter retention coverage.

1.12 Optimization pipeline implementation completed (2026-05-17)

  • Implemented:
    • Added OptimizationPlugin and made PluginType.OPTIMIZATION registry-compatible.
    • Added QuantizationPlugin, PruningPlugin, and FusionPlugin for dynamic quantization, structured pruning, and module fusion.
    • Added OptimizationPipeline to build and execute sequential optimization pipelines from YAML.
    • Added sample configuration config/optimization_pipeline.yaml.
  • Test:
    • Added tests/unit/test_optimization_pipeline.py to verify registration, individual optimizers, and YAML pipeline execution.

1.13 Loihi Brain2Loihi simulator implementation completed (2026-05-17)

  • Implemented:
    • Added Brain2LoihiSimulator backend path to LoihiPlugin.
    • Added run_brain2loihi() execution API.
    • Added config path for backend=brain2loihi_simulator and enable_simulator=true.
    • Added simulator compile metadata retention during convert_format().
  • SDK sample:
    • Added examples/sdk/programs/loihi_brain2loihi_demo.py.
  • Documentation:
    • Added SDK_LOIHI_BRAIN2LOIHI_GUIDE.en.md (build/config/run/troubleshooting).
    • Added navigation in SDK_DEVICE_PLUGIN_GUIDE.en.md, SDK_README.en.md, and SDK_DOCUMENTATION_INDEX.en.md.
  • Test:
    • Added tests/unit/test_loihi_brain2loihi_plugin.py for capability, execution, and convert/deploy validation.

1.14 IBM NeuroChip DevicePlugin implementation completed (2026-05-17)

  • Implemented:
    • Added IBMNeuroChipPlugin as a DevicePlugin path with platform=ibm_neurochip.
    • Added runtime detection for aihwkit and simulator fallback (enable_simulator=true).
    • Added NorthPole profile support via target_chip=northpole with runtime candidates and run_northpole().
    • Added run_simulator() for threshold-based neuromorphic local execution.
  • SDK sample:
    • Added examples/sdk/programs/ibm_neurochip_demo.py.
  • Documentation:
    • Added navigation and usage references in SDK_DEVICE_PLUGIN_GUIDE.en.md, SDK_README.en.md, and SDK_DOCUMENTATION_INDEX.en.md.
  • Test:
    • Added tests/unit/test_ibm_neurochip_plugin.py for capability, simulator, and convert/deploy validation.

1.15 NorthPole runtime hardening and G-QuAT DevicePlugin implementation completed (2026-05-17)

  • Implemented:
    • Enumized NorthPole runtime error codes in IBMNeuroChipPlugin and strengthened structured runtime error reporting via get_capabilities().
    • Added NorthPole mock E2E and CI smoke execution to continuously validate the runtime-adapter path.
    • Added GQuATPlugin (platform=g_quat) with runtime-candidate resolution, API-priority execution, class fallback, output normalization, and simulator fallback.
    • Added .gqpk conversion output and execution APIs run_g_quat() / run_simulator().
  • SDK sample:
    • Added examples/sdk/programs/ibm_neurochip_northpole_mock_e2e.py.
    • Added examples/sdk/programs/g_quat_mock_e2e.py.
  • CI:
    • Added NorthPole E2E execution under northpole_mock and added a new g_quat_mock profile in the optional dependency matrix.
  • Documentation:
    • Extended SDK_DEVICE_PLUGIN_GUIDE.en.md with NorthPole mock and G-QuAT run/config guidance.
  • Test:
    • Validated the NorthPole adapter contract via tests/unit/test_northpole_runtime_adapter.py and integration coverage.
    • Added tests/unit/test_g_quat_plugin.py for metadata, runtime path, simulator fallback, and runtime-failure behavior.

1.16 Q-PFC + QAOA solver plugin system implementation (2026-06-11)

  • Implemented in the repository:

    • Added automatic .env loading (_load_dotenv) and prioritized PluginFactory.SOLVER dispatch in evospikenet/optimizer.py.
    • Added PluginType.SOLVER to evospikenet/plugins/__init__.py.
    • Added SolverPlugin abstract base class at evospikenet/plugins/solver_plugin.py.
    • Added six built-in solver backend plugins under evospikenet/plugins/builtin/solver_backends/:
      • classical_solver_plugin.py — SimulatedAnnealing (no extra deps)
      • qaoa_simulator_solver_plugin.py — classical QAOA approximation using SA/SPSA/greedy (no extra deps)
      • qiskit_solver_plugin.py — Qiskit Aer / IBMQ QPU (requires qiskit)
      • dwave_solver_plugin.py — D-Wave QPU / annealing (requires dwave-ocean-sdk)
      • openjij_solver_plugin.py — OpenJij SA/SQA (requires openjij, connectivity verified)
      • fixstars_amplify_solver_plugin.py — Fixstars Amplify AE (requires amplify, live connection verified)
    • Updated evospikenet/qpfc_qaoa_bridge.py to switch solver backends via get_optimizer().

  • Benchmarks & results:

    • Added EvoSpikeNet-Core/scripts/bench_qpfc_qaoa_bulk.py (n=400 trials per backend) for bulk benchmarking.
    • Bench outputs and statistical reports are stored in EvoSpikeNet-Core/bench_reports/ (JSONL/CSV/MD).

  • SDK & docs:

    • Added SDK sample examples/sdk/programs/solver_plugin_demo.py.
    • Documentation: added Docs/docs/SDK_SOLVER_PLUGIN_GUIDE.md and Docs/docs/SDK_SOLVER_PLUGIN_GUIDE.en.md.

1.17 Memory-learning integrated control design update (2026-08-14)

  • Added Memory and Learning Integrated Control Design to turn the Sparse_Evo_MemoryLM_System_Review.md conclusion into a concrete control-flow design spanning memory retrieval, inference, learning updates, memory write-back, and audit logging.
  • Mapped the minimal evospikenet/memory_orchestrator.py implementation to the supporting assets in specs/system_orchestration_spec.md, specs/memory_event_schema.md, and specs/failure_handling_matrix.md.
  • Documented the migration target that forbids mock / fallback behavior on the production path and terminates explicitly on mandatory-stage failures.
  • Recorded the later Priority1 implementation pass: transaction boundaries, request/session consistency, learning-control wiring, and local regression gates were added on 2026-08-15.

2. Detailed Unimplemented Items

This chapter consolidates items that lack repository implementation or depend on external procedures.

2.1 Phase 1 — Foundation & short-term tasks (unimplemented)

Item Source Status
Large-scale memory extension stress test (thousands of nodes) Section 15 Phase 1 Bench scripts exist but full-scale validation not executed
Long-run stability test (72+ hours) Section 15 Phase 1 Monitoring & auto-recovery exist but long-duration stability trials not performed
SDK additional language bindings (Swift etc.) Section 6.3 / 15 Phase 1 Languages other than Go/TypeScript not created
At-rest data encryption & KMS / secrets management Section 7.4 At-rest key management not implemented
Policy-based network control / zero-trust hardening Section 7.4 OPA-based deny rules exist but full zero-trust architecture not implemented

2.2 Phase 3 — High-level cognition unimplemented items

Item Source Status
Meta-learning (MAML / few-shot) Section 15 Phase 3 / 11.4 Planning only
AI ethics framework: XAI / fail-safe Section 15 Phase 3 / 11.1 Conscience base exists but XAI and dedicated fail-safe layers unimplemented
Social learning / multi-agent coordination enhancements Section 15 Phase 3 / 11.8 Distributed backbone exists but social learning mechanisms unimplemented

2.3 Phase 4 — Research frontier unimplemented items

Item Source Status
Quantum hardware integration (IBM / Google) Section 15 Phase 4 / 11.3 Quantum proxy exists but real-device integration not started
BMI / BCI integration & neural feedback Section 15 Phase 4 / 11.5 EEG integration exists but full BMI / closed-loop integration not started
Robot integration (hardware evaluation pipeline, online evolution) Section 15 Phase 4 / 10.4 / 11.15 Hardware evaluation pipeline not implemented
Hybrid cloud/edge automated deployment Section 15 Phase 4 / 11.6 Planning only
Thousand-node production-scale distributed system Section 15 Phase 4 / 11.12 Production-grade scale-up not implemented
Hybrid quantum-classical systems Section 15 Phase 4 / 11.3 Research concept only

2.3.1 Quantum device-plugin roadmap phases (new)

Phase Item Status
Phase Q-1 Minimal IBMQuantumPlugin + dependency-free fallback Implemented
Phase Q-2 Runtime-backed QAOA/VQE execution and job/result handling Implemented
Phase Q-3 Common optimization layer for quantum device plugins (cost/retry/audit) In progress
Phase Q-4 Hybrid quantum-classical distributed production operation (SLO/observability) Not started

Details: - Implemented in Phase Q-2: - Runtime connectivity - Sampler/Estimator execution APIs - counts/probabilities reconstruction - lightweight QAOA parameter optimization loop - VQE-specific workflow coverage - Follow-up improvements after Phase Q-2: - real-hardware backend constraint handling - durable job persistence/resume/audit integration - Planned for Phase Q-3: - common retry/backoff - cost and shot-budget controls - smarter backend selection policies - standardized execution trace and audit metadata

2.4 Extension themes — unstarted items

The following items are migrated from the original Section 11 (New features & extensions) and remain planned rather than implemented.

2.4.1 AI ethics and safety extensions

AI ethics and safety extensions aim to layer decision transparency, safe-stop behaviors, and policy compliance checks on top of the existing Conscience Circuit and audit-log foundation.

  • Explainability (XAI): Expose decision traces, attention weights, and reward contributions in human-inspectable form.
  • Fail-safe systems: Autonomous safe-stop, privilege reduction, and dangerous-action containment on anomaly detection.
  • Ethical evaluation framework: Pre/post-decision policy and social impact assessments.

2.4.2 Multimodal extensions

Extend current vision/audio/language/brain-language stacks toward bodily perception and robotics.

  • Haptics & force integration: Convert contact/force signals into spike sequences for grasping and manipulation.
  • Proprioceptive integration: Incorporate pose, joint angles, accelerations, and balance into whole-body state estimation.
  • Advanced multimodal fusion: Strengthen cross-modal attention, semantic alignment, and temporal synchronization.

2.4.3 Quantum computing integration

Bridge quantum-inspired components to real quantum backends and hybrid flows.

  • Quantum hardware integration: Interface with IBM/Google quantum backends for job submission and result retrieval.
  • Quantum algorithm optimization: Apply VQE/QAOA-style methods coordinated with SNN/PFC systems.
  • Hybrid quantum-classical systems: Partition workloads between classical and quantum parts.
  • Quantum error correction: Reliability mechanisms for extended quantum runs.

2.4.4 Meta-learning & adaptivity

Improve rapid adaptation in low-data or novel-task regimes; integrate with continual learning and self-evolution.

  • Fast task adaptation: Few-shot internal parameter tuning.
  • Continual meta-learning: Update meta-parameters during operation.
  • Task generalization: Automatic adaptation to novel distributions.
  • Self-supervised meta-learning: Extract adaptation strategies from unlabeled data.

2.4.5 Brain–Machine Interfaces

Extend EEG and Brain Language foundations toward bidirectional BMI/BCI systems, not just readout but feedback.

  • BCI integration: Direct control from brain-wave/neural signals.
  • Neural feedback: Closed-loop return of system state or training signals to the brain.
  • Hybrid BMI: Mixed non-invasive and invasive modalities.
  • Induced neural plasticity: Plasticity-targeting mechanisms for rehabilitation and training.

2.4.6 Cloud / Edge integration

Extend geo-distributed node management to dynamically leverage cloud and edge resources for latency, cost, power and data-sovereignty tradeoffs.

  • Hybrid deployments: Per-node cloud/edge placement switching.
  • Edge AI optimization: Compression and low-power inference for constrained devices.
  • Cloud scaling: Auto-scaling and cost optimization.
  • Distributed cloud: Multi-cloud failover and region-aware optimization.

2.4.7 Self-evolution, sociality, cognitive innovation, and application extensions

A consolidation of late Section 11 items into long-term research and product directions: self-evolution, multi-agent social learning, memory & lifelong learning, robustness, and real-world applications.

  • Self-evolution: Meta-evolution, hierarchical evolution, coevolution, adaptive evolutionary strategies.
  • Social / multi-agent: Coordination, social learning, communication protocols, collective intelligence.
  • Memory & continual learning: Long-term memory, episodic memory extensions, integration, controlled forgetting.
  • Robustness: Dynamic-environment adaptation, noise robustness, fault recovery, predictive adaptation.
  • Human-in-the-loop learning: Human-AI cooperative learning, active learning, interactive training, supervised cooperation.
  • Scalability / novel sensors: Heterogeneous hardware integration, dynamic scaling, extended vision/acoustic ranges, environmental and biometric sensors.
  • Cognitive architecture innovations: Consciousness models, generalized emotion processing, intuition and insight modeling.
  • Real-world applications: Smart cities, medical AI, education AI, environmental monitoring.

2.5 Phase E-3 external-dependency outstanding items

Item Status Note
E-3-5 HCP DUC data procurement & placement External-dependent, not started Placement under data/hcp/ and NPZ conversion require contractual and operational procedures outside the repository

2.6 2026-04-18/19 source-revalidation updates

This section records classification fixes and implementation-plan updates after re-checking current source under evospikenet/, rag-system/, and related tests.

2.6.1 Classification corrections

  • XAI / fail-safe / social-learning / MAML should no longer be treated as "fully untouched" because foundational scaffolding exists in foundation extension modules and API modules; the remaining gap is production-level completion.
  • Security features with concrete implementation (AES-256-GCM + PBKDF2 at-rest path, cert rotation operation APIs) should be tracked as operational-integration pending rather than unimplemented.
  • Additional language bindings status should reflect that Swift bindings exist; remaining gaps are other language ecosystems.

2.6.2 Added implementation-tracking items

Added item Background Expected outcome
Doc/Test/Code status consistency audit Legacy unimplemented labels remained after code changes Reliable priority and quality-gate decisions
Long-run SLO validation (72h+) Features exist but production-runtime evidence is limited MTTR/failure-rate evidence for rollout decisions
Cloud/edge control-plane automation Distributed building blocks exist but orchestration remains weak Better latency/cost/availability tradeoff
Video/audio time-series analysis pipeline Dedicated pose/tracking/action/asr modules were initially absent Practical multimodal workload readiness

2.6.3 Progress update: video-analysis + RAG background jobs

  • 2026-04-18 (video-analysis PoC baseline): added evospikenet/video_analysis/pose.py, tracking.py, action_recognition.py, asr.py, fusion.py, and evospikenet/api_modules/video_analysis_api.py, with new unit/integration tests.
  • 2026-04-18 (video-analysis operational hardening): queue modes auto/redis/sqlite/json, centralized config/video_analysis_config.yaml, and backend-performance metrics exposed at /api/video-analysis/queue/status.
  • 2026-04-19 (RAG background jobs):
    • Added rag-celery-worker and Redis-backed Celery environment wiring in both root docker-compose.yml and rag-system/docker-compose.yml.
    • Added celery/redis runtime dependencies to rag-system/requirements.txt.
    • Hardened rag-system/rag_api.py so /upload_status can return progress even when local in-memory tracker is absent but Redis progress exists.
    • Added Redis-only status coverage in tests/unit/test_rag_status.py.
    • Added rag-system/.env.example for reproducible environment setup.

2.6.4 Remaining gaps after these updates

  • Real model deployment/inference assets for MoveNet/Whisper/ST-GCN are still pending operational packaging.
  • Video-analysis queue backplane still needs final production validation under target infrastructure.
  • CI quality gates tied to threshold metrics are still pending final rollout.
  • RAG Celery/Redis path is implemented; remaining work is operational verification and runbook hardening.

2.6.5 2026-04-21 implementation completion for 2.4.3.a (initial production baseline)

  • Video-analysis infra: implemented sync/async execution path in evospikenet/api_modules/video_analysis_api.py and connected Celery task execution in evospikenet/video_analysis/worker.py.
  • Cloud/edge control plane: implemented scoring breakdown + constraints in evospikenet/orchestration/placement.py, and scenario-driven reconfiguration with snapshot/restore in evospikenet/orchestration/reconfigure_api.py.
  • Ethics/safety: implemented structured explanations (rationale/counterfactuals/safety checks), progressive stop staging, and JSONL audit utilities in evospikenet/ethics/explanation.py and evospikenet/ethics/safety_hooks.py.
  • Specs and operations docs updated: specs/explanation_schema.json, specs/placement_policy.yaml, Docs/ops/placement_runbook.md, Docs/ops/ethics_and_audit.md, Docs/implementation/video_analysis_design.md.

3. Detailed In-Progress Items

This chapter gathers partially implemented, integration-pending, or optimization tasks ordered by phase.

3.1 Phase 1 — Foundation items currently in progress

3.1.1 Brain Language reverse decompilation

  • Current status: Placeholder implementation exists with a word-concatenation decoder and unit tests.
  • Source evidence: evospikenet/eeg_integration/brain_language_decoder.py, evospikenet/eeg_integration/eeg_translator.py
  • Ongoing tasks: Integrate a practical trained model and finalize performance/latency verification.

3.1.2 SDK additional language bindings

  • Current status: Skeletons for Go / TypeScript and Python proxies exist.
  • Source evidence: evospikenet/sdk/go_sdk.py, evospikenet/sdk/ts_sdk.py, tests/unit/test_sdk_stubs.py
  • Ongoing tasks: Expand to Swift and other language bindings.

3.1.3 Security extensions

  • Current status: API Key auth, rate limiting, header-based mTLS, and rotate_certs() implemented.
  • Source evidence: evospikenet/security.py
  • Ongoing tasks: Full certificate distribution, rotation daemon, Vault/Secrets backend integration, at-rest encryption.

3.1.4 Type-safety completion

  • Current status: Type hinting, mypy config, annotation coverage helpers, and CI integration are in place. Static type convergence continues (see Feature 12 section).

3.1.5 EEG device compatibility remaining issue

  • Current status: EEG pipeline improved.
  • Remaining: One disconnect-state transition test for OpenBCI driver.
  • Target: evospikenet/eeg_integration/device_interface.py.

3.2 Phase 2 — Implementation & optimization in progress

3.2.0 MEMERIED SpikeNetLM audio, vision, and multimodal extension — core implemented, integration ongoing

  • Current source-verified boundary:
    • SparseEventMemoryLM is implemented for integer token_ids; it provides INT8 CSR primary synapses, INT16 event state, factorized tied vocabulary, and Top-k routing.
    • SpikingEvoVisionEncoder emits image spike trains, SpikingEvoAudioEncoder emits MFCC-derived spike trains, and AudioToBrainLanguageSystem emits candidate discrete audio tokens.
    • SpikingEvoMultiModalLM remains a separate dense-fusion text/image/audio path and cannot be directly replaced by the sparse text backbone.
  • Implemented:
    • VectorQuantizedTokenBridge, VisionEventTokenizer, AudioEventTokenizer, and AudioLanguageTokenAdapter provide range-checked discrete tokens and validity masks.
    • MultimodalVocabularyLayout, MultimodalTokenBatch, MultimodalSequenceBuilder, and MemoriedSpikeNetLM exclude padding from routing, event state, and local plasticity.
    • Text/image/audio range logits, a pooled classification head, sampled CE, local plasticity, and MemoriedSpikeNetLMConfig model/config/manifest/report artifact upload.
  • Remaining implementation:
    • Trained and version-frozen vision/audio bridges, direct waveform tokenization, dedicated trainer/data collation, and CTC/retrieval heads.
    • Streaming-state cache, API architecture selector, artifact download, and measured VRAM/latency benchmarks.
  • Delivery order:
    • P1: MultimodalTokenBatch, vision/audio bridges, and unified data contract (core complete).
    • P2: MemoriedSpikeNetLM, basic task heads, and SDK artifacts (core complete); trainer/API/additional heads continue.
    • P3: Chunk-level audio/video streaming state, incremental cache, and latency measurement.
  • Detailed plan: MEMERIED SpikeNetLM audio, vision, and multimodal specification

3.2.0.a Configurable LanguageModel d_model and artifact compatibility — code implemented, production rollout pending

  • Implemented:
    • Core saves checkpoint, config.json, tokenizer, and a SHA-256-backed artifact_manifest.json; Core/SDK return durable artifact URIs.
    • Mineral Exploration persists/lists immutable model_spec and manifests, while records without them are explicitly legacy_unverified.
    • UI/API validate architecture settings and \(d_{model} \bmod n_{heads}=0\), then resolve a verified base checkpoint to Core-readable base_model_path for continued training.
    • Continued training and inference preflight verify checkpoint/config/tokenizer presence, digests, and model_spec; legacy, incompatible, and tampered bundles fail with HTTP 422.
  • Remaining operational work:
    • Deploy without modifying currently running jobs, migrate legacy records through an administrator-reviewed manifest, and perform live Docker integration validation separately.
  • Completion criteria:
    • Automated tests prove isolated listing/inference for different d_model artifacts, pre-job rejection of incompatible continued training, and real checkpoint loading only for an identical specification.
  • Detailed specification: LanguageModel Architecture Compatibility and Artifact Contract

3.2.0.b LanguageModel GPU capacity validation — code implemented, production rollout pending

  • Implemented:
    • Mineral compares free VRAM on the GPU currently visible to Core with the estimated requirement for a dense SpikingEvoTextLM request. The capacity estimate and Core execution use the same submitted seq_len.
    • POST /api/language-mode/training-capacity returns a non-mutating capacity query. The UI displays required/free memory after configuration changes and disables training for known overflow.
    • Training-launch APIs repeat this validation and return HTTP 422 with structured capacity data when free memory is insufficient.
    • Core/SDK execute the submitted configuration, including batch_size, without automatic adjustment. An insufficient request does not rewrite the request or artifacts.
  • Remaining operational work:
    • Validate production deployment on real GPUs, monitor races where GPU use changes after the query, and separately design distributed training / multiple-GPU VRAM aggregation.
  • Completion criteria:
    • Keep automated coverage that rejects an insufficient request without changing batch size, preserves submitted seq_len, and accepts the same request with sufficient free H100 memory.
  • Detailed specification: LanguageModel GPU Capacity Validation Policy

3.2.0.c Memory-learning integrated control — minimal orchestration implemented and validated

  • Implemented:
    • evospikenet/memory_orchestrator.py now contains the minimum orchestration types and process_request() flow using MemoryContextPayload, MemoryEventRecord, and FailureHandlingResult.
    • specs/system_orchestration_spec.md, specs/memory_event_schema.md, and specs/failure_handling_matrix.md define the initial contracts.
    • evospikenet/common.py added AuditLogger and DataConverter, and SDK execution examples started using structured audit logging.
    • Added POST /api/memory/orchestrate in evospikenet/api_modules/memory_api.py, enabling retrieval -> execution -> learning -> write-back through one API request.
    • Added runtime backend selection (stub, api_loaded, evolm_backend) and surfaced effective_runtime_model to indicate the backend actually used.
    • Connected OrchestrationRuntimeError mapping in evospikenet/error_handling.py to the standard MLI-* API error envelope.
    • Added API tests in evospikenet/tests/test_memory_api_orchestrate.py covering success, fail-fast true/false, route-level error mapping, and audit-stream consistency.
    • Revalidated the orchestration path in evospikenet/tests/test_memory_orchestrator.py and evospikenet/tests/test_orchestration_error_handler.py; the focused regression set passes under the current runtime and LM alias normalization rules.
    • evospikenet/learning_control_coordinator.py wires Meta-STDP, AEG, ForgettingController, and LongTermMemoryModule through a shared request context.
    • evospikenet/memory_orchestrator.py adds transaction IDs plus transaction_started / transaction_committed / transaction_rolled_back audit events, record normalization for learning, and LTM context propagation.
    • evospikenet/memory_writeback_service.py preserves transaction_id in write-back context.
    • evospikenet/execution_policy.py was corrected after validation exposed a missing logging import that caused 500/NameError failures on the integrated path.
    • Priority1 regression coverage was added in tests/unit/test_priority1_learning_control_unit.py, tests/integration/test_priority1_memory_orchestrator_integration.py, tests/e2e/test_priority1_memory_orchestrate_api_e2e.py, and tests/performance/test_priority1_memory_orchestrator_performance.py.
    • A dedicated CI workflow .github/workflows/priority1-memory-orchestrator.yml and a local Makefile target priority1-memory-orchestrator-test were added.
    • Product/venv validation: make priority1-memory-orchestrator-test passed with 5 tests.
  • Design completed:
  • Remaining operational work:
    • Add Docker-backed live SDK/API validation and real runtime/backend follow-up coverage.
  • Completion criteria:
    • user_input -> retrieval -> context fusion -> model execution -> learning update -> memory writeback runs as a single control flow, and any mandatory-stage failure terminates explicitly without degradation.
    • The current implementation is already verified by automated tests for fail-fast behavior, error envelope mapping, transaction boundaries, request/session consistency, and audit consistency.
  • Detailed design: Memory and Learning Integrated Control Design

3.2.1 Spatial generation service high-precision model swap

  • Current status: FastAPI service running; /generate, model versioning and /quantum/infer endpoints available; NEURAL_COMPONENTS_AVAILABLE=True confirmed.
  • Source evidence: evospikenet/services/spatial_generation_service.py, NeuralLanguageAdapter, SceneEncoderDecoderAdapter, AttentionAdapter
  • Implemented: High-precision mode, output quality assessment, and standardized latency metadata (total_latency_ms, performance) are now exposed on /generate responses.
  • Ongoing tasks: Final 50ms/node operational tuning and production hardening.

3.2.2 Spatial module optimization

  • Targets: evospikenet/spatial/recognition.py, evospikenet/spatial/generation.py, evospikenet/spatial/attention.py
  • Current: Identified hotspots in attention.py; torch.jit.script and CUDA kernels trialed; evospikenet/spatial/cuda_kernels.cu and cuda_kernels.py present; FP16 quant utilities and bench harness prepared.
  • Remaining tasks: Complete integration benchmarks, hit 50ms/node thresholds, finalize docs and operational parameters.

3.2.3 RAG extension residual improvements

  • Current status: Core features completed and operational.
  • Remaining improvements: Delta-view UI polishing, performance testing, resource limits and monitoring.

3.3 Cross-cutting in-progress themes

3.3.1 AI ethics & safety ongoing items

  • Bias detection and mitigation: in progress

3.3.2 Multimodal ongoing items

  • Olfactory / gustatory sensor integration: in progress

3.3.3 Documentation fragmentation remediation

  • Integrated documentation site: in progress
  • Delta UI improvements: in progress

4. Implemented Items

This chapter lists items verified in source as complete, organized by phase (Phase 0..E).

4.1 Phase 0 — Core platform foundations

4.1.1 Core SNN engine

The core SNN engine implements membrane updates, spike emission, synchrony, sparse synapse management, time-aware attention and encoding schemes. Higher-level EvoSpikeNet features are built on top of this foundation.

  • Implemented: LIFNeuronLayer, IzhikevichNeuronLayer, EntangledSynchronyLayer, SynapseMatrixCSR, ChronoSpikeAttention, TAS-Encoding, RateEncoder, and SparseEventMemoryLM.
  • SparseEventMemoryLM uses INT8 CSR primary synapses, INT16 event state, a factorized tied vocabulary, and Top-k expert routing. Adam covers only the vocabulary basis, router, low-rank adapters, and normalization parameters; primary synapses use local Hebbian updates. Select it explicitly with --architecture sparse_event_memory; see the specification.

Implementation notes: - LIFNeuronLayer: basic spiking dynamics. - IzhikevichNeuronLayer: diverse firing patterns. - EntangledSynchronyLayer: quantum-inspired phase synchrony. - SynapseMatrixCSR: memory-efficient sparse connectivity. - ChronoSpikeAttention: time-causal spike attention. - TAS-Encoding / RateEncoder: temporal and rate encoders.

4.1.2 Learning and plasticity

Complete learning infra including short-term rules, long-term stabilizers, and energy-aware controllers. Distributed brain, evolution and memory subsystems depend on this layer.

  • Implemented: STDP, Meta-STDP, Homeostasis, MetaPlasticity, EnergyManager, EnergyConstrainedPlasticityController, Surrogate Gradients.

Implementation notes: - STDP: local timing-based synaptic updates. - Meta-STDP: online tuning of STDP rules. - Homeostasis / MetaPlasticity: avoid runaway firing and learning collapse. - EnergyManager: energy budgets influence learning and fitness.

4.1.3 Distributed processing & communication

Coordination across nodes, discovery, load balancing and geo-distributed operation.

  • Implemented: Zenoh-based distributed messaging, PFC control, hierarchical modules, RaftConsensus, AsyncZenohComm, ZenohNodeDiscovery, auto node cleanup, GeoNodeManager, AI-driven load balancing, compression framework.

Implementation notes: - Zenoh/AsyncZenohComm: asynchronous pub/sub backbone. - RaftConsensus: control-plane consistency. - ZenohNodeDiscovery: heartbeat-driven dynamic discovery. - GeoNodeManager: cross-region failover.

4.1.4 Text, multimodal & Brain Language

Perception-to-internal Brain Language mapping and back to action/text.

  • Implemented: WordEmbeddingLayer, SpikingTransformerBlock, SpikingEvoTextLM, SpikingMultiModalLM, fusion components, Vision-to-Language Encoder, Brain Language Processor, Language-to-Motor Decoder.

Implementation notes: - Spike-based language understanding and generation. - Multimodal fusion for semantic alignment.

4.1.5 Brain-function simulation

Modules emulating brain regions for cognition, learning, memory and embodied interaction.

  • Implemented: Visual/Auditory/Language/Speech/Motor/Compute modules, Async-FedAvg, EmbodiedPLA, EpisodicMemoryNode, SemanticMemoryNode, MemoryIntegratorNode.

4.1.6 UI, RAG, SDK foundations

Operational interfaces and developer ergonomics: Dash UI, realtime 3D visualization, settings UI, EvoRAG/Milvus, OpenAPI generation, Jupyter integration, Evolution dashboard.

4.1.7 Security, monitoring & ops

Production-grade operational features: API Key auth, rate limiting, CORS, security validator, Prometheus health checks, profiling, unified exception handling, SpikeEncryption, audit logs, auto recovery, CI/IaC/DB tooling, bilingual support. Operational templates and diagnostics cover video-analysis strict backend readiness, EEG PTP precision checks, mTLS proxy header forwarding status, and alert channel configuration. - API Key auth refreshes env-backed keys on verify, so long-lived processes can rotate EVOSPIKENET_API_KEYS without restart.

4.1.8 DevicePlugin extension baseline (2026-05-16)

  • Implemented IBM quantum-oriented device plugin baseline (IBMQuantumPlugin).
  • Implemented QAOA-inspired neuron layer baseline (QAOANeuronLayer) and plugin wrapper.
  • Added SDK template sample for custom device plugins.

4.1.9 IBM Runtime and QAOA execution baseline (2026-05-16)

  • Added Runtime-backed Sampler/Estimator APIs to IBMQuantumPlugin.
  • Added counts/probabilities reconstruction and lightweight optimization to QAOANeuronLayer.
  • Added IBM Runtime demo SDK sample and live CI connectivity scaffolding.

4.1.10 Optimization pipeline baseline (2026-05-17)

  • Added OptimizationPlugin and PluginType.OPTIMIZATION registry support.
  • Added QuantizationPlugin, PruningPlugin, and FusionPlugin.
  • Added OptimizationPipeline for YAML-defined sequential optimization execution.
  • Added config/optimization_pipeline.yaml and tests/unit/test_optimization_pipeline.py.

4.3 Phase 2 — Biomimetic core completed items (11-1..11-19)

This set corresponds to the biomimetic items previously numbered 11-1 through 11-19 and has been implemented and integrated.

4.3.1 11-1 Delay & Brain Rhythm Introduction

  • Status: Complete
  • Summary: Models axonal conduction delays and phase synchronization; implements multi-band power metrics and PLV; exposes Zenoh delay tags. Implemented in rhythm_sync.py and available via BiomimeticAdapter.rhythm_metrics().

4.3.2 11-2 Cellular & Synaptic Diversification

  • Status: Complete
  • Summary: Diverse inhibitory subtypes, NMDA/AMPA/GABA dynamics, short-term plasticity, and astrocytic modulation integrated.

4.3.3 11-3 Layered / Hierarchical Topology

  • Status: Complete
  • Summary: Cortical-column and layered templates with local and long-range connectivity generation.

4.3.4 11-4 Neuromodulators & Plasticity Gating

  • Status: Complete
  • Summary: Neuromodulator gates (DA/NA/ACh etc.) control STDP/learning rates. Implemented as NeuromodulatorGate, AcetylcholineModule and integrated via BiomimeticAdapter.modulatory_gain().

4.3.5 11-5 Memory System Extensions

  • Status: Complete
  • Summary: Hippocampal-like episodic buffer, prioritized replay and frontal working memory blocks with cortex integration.

4.3.6 11-6 Sensory–Motor Closed-loop Enhancements

  • Status: Complete
  • Summary: Biologically-inspired pre-processing (DoG/Gabor, cochlear filters), efference copy and proprioceptive feedback.

4.3.7 11-13 Emotion & Affective Systems

  • Status: Complete
  • Summary: Amygdala/ACC/insula-like valuation system that biases attention, decisions and consolidation by affective value.

4.3.8 11-14 Sleep-phase Memory Consolidation

  • Status: Complete
  • Summary: Offline replay and slow-wave/SWR-driven consolidation implemented in sleep_consolidation.py.

4.3.9 11-15 Mirror Neuron System

  • Status: Complete
  • Summary: Observation-to-action mapping for imitation learning and social cognition.

4.3.10 11-16 Acetylcholine Module

  • Status: Complete
  • Summary: ACh system for attention/encoding linked to theta rhythms.

4.3.11 11-17 NAcc/VTA Loop (Motivation & Reward)

  • Status: Complete
  • Summary: TD-error dopamine release model and motivation-driven action selection integrated with dynamic goal selection.

4.4 Phase 3 — Higher-cognitive completed items

4.4.1 11-7 Energy & Homeostasis Constraints

  • Status: Complete
  • Summary: Node energy budgets, firing penalties and energy-aware fitness implemented (energy_homeostasis.py).

4.4.2 11-8 Developmental Dynamics

  • Status: Complete
  • Summary: Critical-period schedules, pruning, myelination-like speed changes implemented via DevelopmentalSchedule.

4.4.3 11-9 Intent Representation Module

  • Status: Complete
  • Summary: PFC/ACC-like intent vectors with APIs and history logging.

4.4.4 11-10 Creativity / Generation Engine

  • Status: Complete
  • Summary: Memory recombination and novelty scoring for generative outputs.

4.4.5 11-11 Self-Awareness / Introspection Layer

  • Status: Complete
  • Summary: Meta-state storage, introspection APIs and dashboards for self-monitoring.

4.4.6 11-12 Dynamic Goal Selector

  • Status: Complete
  • Summary: Bandit/choice layer for goal switching with cost-aware selection.

4.4.7 11-18 DMN Dedicated Module

  • Status: Complete
  • Summary: Idle self-referential activity and future-simulation module.

4.4.8 11-19 Curriculum Scheduler

  • Status: Complete
  • Summary: Curriculum schedules linked to developmental plasticity scaling.

4.10 Patent-backed Technology — Implemented Group

The MT25-EV001..MT25-EV030 patents listed in the original document have been confirmed implemented. Key mappings and brief implementation notes are provided below.

4.10.1 Core algorithms & distributed-brain patents

Patent Implemented As
MT25-EV001 ChronoSpikeAttention Causal masking + exponential decay spike attention
MT25-EV002 TAS-Encoding Time-adaptive spike encoding pipeline
MT25-EV003 Quantum PFC Quantum-gate based PFC optimization hooks
MT25-EV004 Energy Plasticity Energy-constrained plasticity with β(t) scaling
MT25-EV005 Hierarchical Brain Rank-based distributed brain pipelines
MT25-EV006 Multi-PFC Cluster Raft-backed HA PFC clusters
MT25-EV007 Embodied PLA Streaming perceptual loop and closed-loop controllers
MT25-EV008 Q-PFC Loop Quantum modulation integrated with uncertainty estimation
MT25-EV009 EvoGenome Structural-adaptive evolutionary engine
MT25-EV010 Brain Language Multi-modal internal language processing

4.10.2 Control, safety & knowledge integration patents

Patent Implemented As
MT25-EV011 Adaptive Gating Load-aware power-saving gates
MT25-EV012 Conscience Circuit Ethical-safety guard rails
MT25-EV013 SNN-RAG Hybrid On-device retrieval augmentation for spike-based systems
MT25-EV014 Universal Integration Unified API & cross-platform adapters
MT25-EV015 Spike Encryption PSK/DH key exchange + AES-256-GCM and forward secrecy
MT25-EV016 Meta-STDP Meta-plasticity enhanced STDP

4.10.3 Biomimetic & high-cognition patents

Patent Implemented As
MT25-EV017 Sleep-phase consolidation WAKE/NREM/REM memory consolidation cycles
MT25-EV018 Neuromodulator Multi-Gate STDP DA/NA/ACh/5-HT/OT gated learning
MT25-EV019 Memory Recombination Memory recombination + novelty scoring engine
MT25-EV020 Cortical Column Topology L1-L6 cortical grid topology and long-range links
MT25-EV021 Developmental Curriculum Scheduler Stage-adaptive pruning & myelination scheduler
MT25-EV022 Adaptive Gain Efference Copy Efference-copy based sensory filtering
MT25-EV023 Emotion-modulated consolidation Amygdala-hippocampal emotional modulation of memory
MT25-EV024 Mirror Neuron Transfer Observation-to-action transfer with imitation reward
MT25-EV025 Retention Scoring for Forgetting Prevention Four-factor retention scoring system
MT25-EV026 Basal Ganglia Value-Cost Selection Expected-value vs cost switching model
MT25-EV027 Retinal/LGN/V1 preprocessing pipeline DoG/Gabor/Gammatone pre-processing
MT25-EV028 PTP Time Sync distributed spike timing IEEE-1588 PTP nanosecond sync integration
MT25-EV029 Coevolution optimization system Multi-population competitive/cooperative evolution
MT25-EV030 Self Meta-Evaluation Layer Introspection vector + degradation detection

Note: Feature 12, Feature 13, Phase E, and biomimetic items 11-1..11-19 are detailed further in other sections of this document.

4.11 Other completed peripheral features

Completed peripheral items include RAG, audit, auto-recovery, quantum proxy, distributed node ops and sensor plugin mechanisms.

  • Sensor plugin framework
  • Markdown parsing and .md handling
  • RAG ingestion, versioning and delta workflows
  • Large-file streaming & background processing
  • evospikenet/audit_log.py
  • evospikenet/auto_recovery.py
  • evospikenet/quantum/quantum_interface.py
  • evospikenet/geo_node_manager.py
  • frontend/pages/evolution_dashboard.py
  • evospikenet/video_analysis/backends.py
  • evospikenet/eeg_integration/eeg_streaming_pb2.py
  • evospikenet/eeg_integration/eeg_streaming_pb2_grpc.py
  • evospikenet/eeg_integration/distributed_brain_executor.py and BiomimeticAdapter
  • Communication wrappers in evospikenet/communication.py and evospikenet/distributed.py
  • EVOSPIKENET_TOKENIZER_NAME override for default tokenizer selection
  • RAG duplicate suppression with legacy exact-text fallback
  • EpisodicMemory.set_max_memories() and runtime eviction policy control

This English edition faithfully mirrors the Japanese document's 4-category reorganization and preserves descriptive detail. If you want different wording (more literal or more concise) for specific subsections (e.g., Feature 12, Feature 13, Phase E tables), tell me which section to prioritize and I will update it next.