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EvoSpikeNet Product Overview

[!NOTE] For the latest implementation status, please refer to Functional Implementation Status (Remaining Functionality).

This document describes the EvoSpikeNet product concept, architecture, and phased features. This is a document to explain from a bird's-eye view, as well as implementation history and version control. Does not include.

Author: Masahiro Aoki (Moonlight Technologies Inc.)

Table of Contents

1. Product concept

EvoSpikeNet is a distributed evolutionary neuromorphic framework and platform for scaling neuroplastic spiking neural networks (SNNs) from the cloud to the edge. It has an architecture that approximates a "whole brain" by linking modules that mimic cortical and subcortical areas on a single device or large-scale cluster, and realizes sensing, cognition, decision-making, and motor control loops in real time. Dynamic genome representation (L5 Evo Genome) and next-generation evolution engine (Evolution v2) enable collaboration across hundreds of nodes via Zenoh/messaging while adapting network structure and learning rules on the fly. It is characterized by the transparent combination of GPU/CPU/FPGA, and the ability to add functions and operate without interruption through plug-in architecture + microservices. Furthermore, we will gradually integrate biomimetic modules (emotional circuits, rhythm synchronization, intention/motivation, cerebellar error correction, etc.) to provide a structure that makes it easy to escape from the AI ​​black box.

Features

The key characteristics that set EvoSpikeNet apart from other platforms are: It clearly shows the product's core technology and design principles.

  • Event-driven processing: In contrast to static calculations in ANN, SNN only processes spikes.
  • Nonlinear dynamics: Expressing various neural behaviors using the Izhikevich model
  • Energy optimization: Cognitive load control with EnergyManager
  • Dynamic Routing: Task allocation by PFC
  • Ultra-low consumption: Energy usage less than 1 μJ per task
  • Biomimetic function group: A modular design that gradually integrates the higher cognitive characteristics of the human brain, such as plastic gating based on emotion/reward/sleep/rhythm synchronization, mirror nervous system, intention vector, cortical topology generation, creativity/DMN/introspection layer, dynamic goal selection, developmental dynamics and curriculum, sensory-motor loop, and acetylcholine system, has been implemented in Phases 2 to 4.

Notes (see source)

Below is an example of the correspondence between major technical elements and representative implementation files. For details, Remaining_Functionality.md contains implementation and test references for each function.

  • Core SNN / Neuron layer: evospikenet/core.py (LIF/Izhikevich layer, SynapseMatrixCSR)
  • Plasticity/learning rules: evospikenet/plasticity.py, evospikenet/learning.py, evospikenet/surrogate.py (STDP / meta‑STDP / surrogate gradients)
  • Evolution Engine: evospikenet/evolution_engine.py, evospikenet/insight.py (GenomePool / Mutation / Crossover / Insight)
  • Prefrontal cortex (PFC) control and quantum modulation: evospikenet/pfc.py, evospikenet/executive_control.py (PFCDecisionEngine, Q‑PFC loop, QuantumModulationSimulator)
  • Spatial processing (Feature 13): evospikenet/spatial_processing.py, evospikenet/spatial/* (SpatialWhere/What/Integration nodes)
  • Biomimetic overlay (EEG → Command): evospikenet/eeg_integration/distributed_brain_executor.py, evospikenet/biomimetic/* (BiomimeticAdapter, modulatory, rhythm_sync, hippocampal_memory)
  • RAG / Data Distillation: evospikenet/distillation.py, evospikenet/rag_milvus.py (LLM synthetic data/Milvus integration)
  • Visualization/UI: frontend/pages/ (Dash dashboard, evolution/visualization/model management)

Each module has unit tests and integration tests, and there are many validation cases under tests/unit/ and tests/integration/.

Application field

  • Robotics: Closed-loop control that combines low-latency motion planning and efference copying achieves fast response and stable control. Representative implementations: evospikenet/embodied_pla_loop.py, evospikenet/motor_efference.py, frontend/pages/distributed_brain.py.
  • Neurointerface (BMI/EEG): Applicable to rehabilitation and prosthetic limb control through rhythm extraction from EEG, reflection of modulator gain, and learning control via sleep buffer. Representative implementation: evospikenet/eeg_integration/distributed_brain_executor.py, evospikenet/biomimetic/modulatory.py.
  • Smart Edge/IoT: Enhance edge analysis with a group of plug-ins that handle sensor diversity (camera/LiDAR/voice/environment) and power-saving inference using lightweight SNN. Representative implementations: evospikenet/parsers/, evospikenet/data_loader.py, evospikenet/core.py.
  • Cognitive science/neuroscience research: Publish parameters such as neuron model, plasticity coefficient, time synchronization, etc. to support reproduction experiments (evospikenet/core.py, evospikenet/plasticity.py, docs/NEUROSCIENCE_BRAIN_SIMULATION_PAPER.md).
  • Security/Monitoring: Behavior analysis and anomaly detection using spatial cognition and continuous learning. Representative implementation: evospikenet/spatial_processing.py, evospikenet/attention.py.
  • Autonomous driving/mobile bodies: Support safe decision making with ChronoSpikeAttention and PFC control that maintain temporal causality (evospikenet/attention.py, evospikenet/pfc.py).
  • Collaborative work/distributed AI: Realize collaborative learning and task distribution of multiple nodes using Zenoh-based distributed mesh and Raft consensus (evospikenet/distributed.py, evospikenet/pfc.py, evospikenet/evolution_engine.py).

Note: For each application field, a corresponding service/API (evospikenet/api.py, frontend/pages/) and integration tests are provided, and scalability is designed for actual operation.

Future vision

By combining multiple functions, edge devices with built-in quantum brains will become a reality. These devices are installed in IoT sensors and robots, and have the ability to integrate diverse sensory information such as visual, auditory, and linguistic information on the fly, and continuously evolve their own neural structures and operating rules.

Implementation status and reference documentation

  • Implementation status: This document focuses on a product overview, but please refer to the implementation status document in the repository root for the latest information on implementation and testing status. Please check Remaining_Functionality.md for detailed completion/partial implementation/unimplemented status of each function.
  • Biomimicry/brain simulation technology: Technical explanations of biomimetic modules (emotions, rewards, sleep, rhythm synchronization, mirror neurons, intentions, developmental dynamics, etc.) and cortical mapping are detailed in docs/NEUROSCIENCE_BRAIN_SIMULATION_PAPER.md.
  • Biomimicry Implementation Plan: The biomimicry enhancement plan and implementation evidence equivalent to Section 11 are organized in the relevant section of Remaining_Functionality.md ("Biomimetic Enhancement Plan") and the implementation audit file. Please refer to the same file for a list of implemented modules and test references.
  • Reference links (implementation evidence): For references to unit/integration tests and specific implementation files, the file path and test name are listed in each item in Remaining_Functionality.md. See docs/NEUROSCIENCE_BRAIN_SIMULATION_PAPER.md for rationale and formulas for neuroscience.

Addendum: Auto Node Mapper — Product Feature Overview

Phase E-0/E-1/E-2 connectome infrastructure completed (2026-03-19): The core infrastructure of this function (connectome_loader.py, connectome/node_mapping.py, SparseDelayBuffer, ConnectomeMetadataPublisher, config/connectome_config.yaml) has all been implemented. scripts/auto_node_mapper.py CLI body and management UI will be implemented in Phase E-3.

Purpose: Provide a function that can automatically generate distributed brain node configurations using public connectome data at the product level. This allows researchers to launch nodes with topologies derived from real data, increasing comparative experiments and reproducibility.

Main features:

  • Auto Node Mapper CLI (planned for Phase E-3): Receive connectome NPZ/JSON as input and output node_manifest.yaml and per-rank NPZ
  • Management UI (future): visualization of node assignments, manual approval workflows (for HCP DUC, etc.)
  • ✅ Configuration: Define resource limit for each rank by rank_profiles in config/connectome_config.yaml (implemented)
  • ✅ Verification: E/I ratio/size verification by apply_to_layer(), validate_ei_ratio() implemented

Advantages (product benefits):

  • Ensure biological validity of experiments by designing nodes based on real data
  • Reduce installation costs by automating 29 node deployment
  • Improved inter-node reproducibility and improved operability

Implementation roadmap (product perspective):

  1. ✅ Infrastructure PoC (C. elegans): ConnectomeLIFLayer・SparseDelayBuffer・ETag cache completed (Phase E-1/E-2)
  2. CLI (Phase E-3): manifest output with scripts/auto_node_mapper.py
  3. Enterprise integration: HCP DUC procedure/approval workflow integration
  4. UI integration: manifest / mask status display on Dashboard

Security note: Contract data such as HCP must be stored encrypted and access audited. The production version will integrate the manual approval flow into the UI.

2. Overall architecture

Architecture diagram

flowchart TD
    subgraph P1["Phase 1‑3: Core / Evolution / Energy"]
        Core["SNNModel\nLIF/IzhikevichNeuronLayer\nSynapseMatrixCSR\nPlugin Loader"]
        Evol["Evolution v2 Engine\n(GenomePool/MutationEngine/CrossoverEngine)\nInsightEngine"]
        Energy["EnergyManager\nE_Cost = α·ΣSpikes + β·H(P)"]
        NodeDisc["NodeDiscovery\nLoadBalancer\nZenoh Heartbeat"]
    end

    subgraph P2["Phase 4‑5: Text / Quantum / EEG"]
        Text["TASEncoderDecoder\nWordEmbeddingLayer\nRateEncoder"]
        Quantum["EntangledSynchronyLayer\nQuantumModulationSimulator\nQ‑PFC Loop"]
        EEG["EEGIntegration\nBrainLanguageEncoder\nDistributedBrainExecutor"]
    end

    subgraph P3["Phase 6‑7: Learning / Distillation / Multimodal"]
        Grad["Surrogate Gradients\nMetaSTDP / AEG"]
        Distill["model_compressor.py\nMilvus DB"]
        Multimodal["SpikingEvoMultiModalLM\nVision/Audio/Text Fusion\nChronoSpikeAttention"]
    end

    subgraph P4["Phase 8‑10: Brain Distributed System"]
        PFC["PFCDecisionEngine\nRaftConsensus\nTask Control\nθ_E Control"]
        Modules["Visual/Auditory/Language/Speech/Motor/Compute\nAsync‑FedAvg"]
        QSync["Q‑PFC Loop\nSpike Distillation"]
    end

    subgraph P5["Phase 11: UI / RAG"]
        UI["Dash UI\nVisualization / Data Input\n25+ Pages"]
        RAG["EvoRAG\nMilvus Integration\nLLM Backends"]
    end

    Core --> Evol --> Energy --> NodeDisc --> Text --> Quantum --> Grad --> Distill --> Multimodal --> PFC
    PFC --> Modules
    Modules --> UI & RAG

Plugin architecture and microservices

EvoSpikeNet moves from a monolithic structure to a plugin architecture and microservices, significantly improving development efficiency and scalability [...]

Plugin architecture

  • Dynamic plugin system: Detects and loads plugins at runtime, allowing functionality to be added without stopping the system (including Evolution v2 / Genome module)
  • 7 different plug-in types: NEURON, ENCODER, PLASTICITY, FUNCTIONAL, LEARNING, MONITORING, COMMUNICATION, (plus Evolution)
  • 70% reduction in new feature addition time: average 4-5 days → 1-1.5 days
  • Extensibility: Easy to add custom plugins and supports automatic integration via entry_points

Microservices

                   ┌─────────────┐
                   │ API Gateway │
                   │  Port 8000  │
                   └──────┬──────┘
                          │
         ┌────────────────┼────────────────┐
         │                │                │
    ┌────▼────┐     ┌────▼────┐     ┌────▼────┐
    │Training │     │Inference│     │Registry │
    │Port 8001│     │Port 8002│     │Port 8003│
    └─────────┘     └─────────┘     └─────────┘
         │                │                │
         └────────────────┼────────────────┘
                          │
                   ┌──────▼──────┐
                   │ Monitoring  │
                   │  Port 8004  │
                   └─────────────┘
  • Training Service (Port 8001): Model training job management, distributed training coordination, checkpoint management
  • Inference Service (Port 8002): Inference processing, model caching, dynamic batching
  • Model Registry Service (Port 8003): Model version control, metadata management, file storage
  • Monitoring Service (Port 8004): Metric collection and aggregation, alert management, dashboard provision
  • API Gateway (Port 8000): Request routing, load balancing, service discovery
  • 80% scalability improvement: Easier horizontal scaling, resource efficiency 60% → 85%

Details: Plugins & Microservices Architecture

Static analysis integration

A comprehensive static analysis platform that automatically ensures code quality:

  • Integration of 7 analysis tools: Black, isort, Flake8, Pylint, mypy, Bandit, interrogate
  • Pre-commit hook: Perform automatic checks before commit
  • CI/CD integration: Automatic quality checks and report generation with GitHub Actions
  • Quality Dashboard: Visualize results in HTML
  • Quality threshold: Pylint ≥7.0, security issues ≤5, Flake8 issues ≤50, Docstring coverage ≥60%
  • Expected effect: Code quality improvement 70%, review time reduction 50%

Details: Code Quality Guide

Infrastructure as Code (IaC)

Comprehensive infrastructure automation platform for 100% environmental reproducibility:

  • Terraform integration: Docker provider, network/volume management, environment variable automatic generation
  • Ansible integration: System setup automation with 20+ tasks
  • Kubernetes Ready: Production manifest and autoscaling
  • Multi-environment support: Clear separation of Dev/Staging/Production
  • One-command operation: make env-setup, make terraform-apply, etc.
  • Expected effect: 100% environmental reproducibility, 90% reduction in setup time

Details: Infrastructure as Code Implementation Guide

Configuration externalization (Configuration Management)

Dynamic configuration management system that increases operational flexibility by 90%:

  • Pydantic-based type safety settings: Type definition and automatic validation for all setting items
  • Multi-layer configuration loading: Environment variables > YAML > Default priority
  • Hot reload: Reflects configuration changes without restarting the server
  • 6 category comprehensive settings: Database, API, Model, Zenoh, Hardware, Monitoring
  • Environment-specific configuration file: Development/Staging/Production
  • 7API endpoints: /api/config/current (get configuration), /api/config/update (update), etc.
  • Expected results: 80% reduction in environment construction, 95% reduction in setting changes, 90% reduction in setting errors.

Details: Configuration externalization implementation guide

Refinement of load distribution

Dynamic load balancing system between multiple instances of the same module type:

  • 5 types of distribution strategies: minimum response time, weighted round robin, consistent hashing, dynamic capacity, queue length based selection
  • Instance pooling: Managed by module type
  • Real-time metrics: Monitor response time, throughput, queue length, and error rates
  • Adaptive Capacity Management: Automatically adjust according to load
  • Zenoh integration: Node discovery and load-aware routing
  • Performance improvement: 25% increase in throughput, 24% reduction in response time, 60% reduction in error rate

Details: Dynamic Load Balancing Guide

3. Feature list (by phase)

Organized EvoSpikeNet functions in phases 1-11. Each phase describes the future image based on the combination of technical logic and functionality.

Phase 1: Core SNN Engine

  • LIFNeuronLayer, IzhikevichNeuronLayer: Nonlinear neurodynamic model ✅ Implementation completed (evospikenet/core.py)
  • SynapseMatrixCSR: Efficient connection management using sparse matrices ✅ Implementation complete (evospikenet/core.py)
  • SNNModel: Multi-layer integration and timestep transition ✅ Implementation completed (evospikenet/core.py)
  • Future Vision: Real-time brain imitation on low-power edge devices

Phase 2: Dynamic evolution and visualization

  • STDP: Timing-dependent plasticity ✅ Implementation completed (evospikenet/plasticity.py)
  • DynamicGraphEvolutionEngine: Synapse evolution algorithm ✅ Implementation completed (evospikenet/evolution_engine.py)
  • InsightEngine: Real-time visualization ✅ Implementation completed (evospikenet/insight.py)
  • Future Vision: Adaptation to unknown environments with self-evolving AI

Phase 3: Energy-driven computing

  • EnergyManager: Ignition restriction and energy monitoring ✅ Implementation complete (evospikenet/energy_plasticity.py)
  • Cognitive load calculation: Spike activity and entropy integration ✅ Implementation completed (evospikenet/energy_plasticity.py)
  • Temperature control: Routing according to energy state ✅ Implementation completed (evospikenet/pfc.py)
  • Future Vision: Long-term operation on battery-powered devices

Phase 4: Text processing

  • WordEmbeddingLayer: Text vectorization and positional encoding ✅ Implementation complete (evospikenet/embedding.py)
  • RateEncoder: Convert input intensity to Poisson firing ✅ Implementation completed (evospikenet/encoding.py)
  • TASEncoderDecoder: Time adaptive spike encoding/decoding ✅ Implementation complete (evospikenet/encoding.py)
  • WordEmbeddingLayer: Text vectorization and positional encoding ✅ Implementation complete (evospikenet/text.py)
  • RateEncoder: Convert input intensity to Poisson firing ✅ Implementation completed (evospikenet/text.py)
  • TASEncoderDecoder: Time adaptive spike encoding/decoding ✅ Implementation complete (evospikenet/encoding.py)
  • PerceptionToTextConverter: Text generation from visual/audio features ✅ Implementation completed (evospikenet/brain_language.py)
  • Future Vision: Hybrid AI of natural language and vision

Phase 5: Quantum Inspired Features

  • EntangledSynchronyLayer: Quantum-inspired synchronization with FFT-based context modulation ✅ Implementation complete (evospikenet/pfc.py)
  • QuantumModulationSimulator: PFC feedback coefficient generation by single qubit simulation ✅ Implementation completed (evospikenet/pfc.py)
  • Q-PFC Loop: Dynamic γ adjustment based on cognitive entropy ✅ Implementation completed (evospikenet/pfc.py)
  • Quantum expectation feedback: ⟨Z⟩ reflected in neuron dynamics ✅ Implementation completed (evospikenet/pfc.py)
  • Future Vision: Ultra-high-speed decision making with quantum computing fused AI

Phase 6: Gradient-based learning

  • Surrogate Gradients: Approximation of spike discontinuities ✅ Implementation completed (evospikenet/learning.py)
  • Surrogate Gradients: Approximation of spike discontinuities ✅ Implementation complete (evospikenet/surrogate.py)
  • STDP integration: Timing dependent learning ✅ Implementation completed (evospikenet/plasticity.py)
  • Future Vision: High accuracy learning with low data

Phase 7: Data Distillation

  • distillation.py: Synthetic data generation using LLM backend ✅ Implementation completed (evospikenet/distillation.py)
  • Knowledge base insertion: Spike distillation data to Milvus DB ✅ Implementation complete (evospikenet/rag_milvus.py)
  • Future Vision: Personalized AI with private data

Phase 8: Quantum Spike Processing

  • distributed_qpfc.py: Quantum PFC distributed decision system ✅ Implementation completed (evospikenet/distributed_qpfc.py)
  • Quantum PFC: Decision making using quantum states ✅ Implementation completed (evospikenet/executive_control.py)
  • Future Vision: Massively parallel processing with quantum computing

Phase 9: Brain function distributed system & spatial cognition integration 🎯

  • PFCDecisionEngine: Task control and decision-making engine ✅ Implementation completed (evospikenet/pfc.py)
  • RaftConsensus: Leader election and failover for PFC high availability (<5 seconds) ✅ Implementation complete (evospikenet/pfc.py)
  • Functional module architecture: Distributed processing using VisualModule, AuditoryModule, MotorModule, SpeechGenerationModule, etc. ✅ Implementation completed (evospikenet/functional_modules.py)
  • Asynchronous FedAvg: aggregation with routing probabilities and spike distillation
  • Cognitive load feedback: temperature control and task deferral based on energy status
  • Long-term memory layer: episodic/semantic memory (episodic_memory.py)
  • Episode Memory System ⭐ (January 23, 2026): EpisodicMemoryNode, SemanticMemoryNode, MemoryIntegratorNode fully implemented
  • AEG-Comm communication optimization ⭐ (January 23, 2026): 3-layer safety architecture integration
  • SNN memory expansion (February 26, 2026):
  • LargeScaleSpikeReservoir: Spike compression and retention ✅ (evospikenet/memory_spike_reservoir.py)
  • ForgettingController: Time constant-based forgetting control ✅ (evospikenet/forgetting_controller.py)
  • LongTermMemoryModule: Long-term episode integration from spikes ✅ (evospikenet/long_term_memory.py)
  • LocalMemoryClient: Access without HTTP with offline SDK helper ✅ (evospikenet/sdk.py)

Feature 13: Spatial recognition/generation system ⭐ 🎯 (implementation completed on February 17, 2026): - Rank 12 (SpatialWhereNode): Parietal lobe dorsal pathway, spatial position/depth/coordinate transformation, processing latency <50ms ✅ - Rank 13 (SpatialWhatNode): Visual/temporal cortex, object recognition 100+ classes/scene generation, processing latency <30ms ✅ - Rank 14 (SpatialIntegrationNode): Occipito-Parietal Junction, What-Where Integration/World Model Construction, Processing Latency <50ms ✅ - Rank 15 (SpatialAttentionControlNode): Fronto-orbital area, saccade planning/spatial attention control, processing latency <30ms ✅ - Implementation details: - Code size: 891 lines (evospikenet/spatial_processing.py) - Test coverage: 17+ test cases, 100% success rate - Model switching & high precision mode: Dynamically load high precision encoder/decoder with model_version request field. Language/scene/attention adapters switch behavior with flags and give high_precision/quality_factor to output. - Performance: 7.1x acceleration with CUDA attention kernel + scripted fallback. Benchmark targeting 50ms/node or less using FP16/mixed-precision support and quantum auxiliary path. - Zenoh integration: asynchronous communication of all 4 nodes, PTP time synchronization - End-to-end delay: 150-200ms (equivalent to human reaction time)

  • Future Vision: Multitasking AI with human-like spatial cognition and motor control

Phase 10: Multimodal expansion and spatial-cognitive integration

  • SpikingEvoMultiModalLM: Text/visual/audio/spatial integrated multimodal model ✅ Implementation completed (evospikenet/models.py)
  • ChronoSpikeAttention: Causal spiking attention mechanism using temporal proximity mask ✅ Implementation completed (evospikenet/attention.py)
  • Inter-module cooperative control: Sensory integration using a hierarchical fusion layer of visual-spatial-cognitive ✅ Implementation completed (evospikenet/fusion.py)
  • Spatial-language interface: Integrate spatial information from Rank 12-15 into language processing of Rank 16-22 ✅ (evospikenet/spatial_language_bridge.py)
  • Dataset integration: SHD/TIMIT/ImageNet/MNIST + spatial benchmark ✅ Implementation completed (evospikenet/data_loader.py)
  • Visualization tool integration: 28-page interactive dashboard with Dash UI ✅ Implementation completed (frontend/pages/)
  • Future vision: Full brain AI with visual-spatial cognitive integration

Phase 11: Web UI/RAG Integration

  • Dash-based UI: 28-page interactive dashboard ✅ Implementation completed (frontend/pages/)
  • distributed_brain.py: Distributed brain simulation ✅ Implementation completed (frontend/pages/distributed_brain.py)
  • rag.py: RAG query interface ✅ Implementation complete (frontend/pages/rag.py)
  • Data management UI: Streaming/background upload and session ID support ✅ (When making an internal call, specify the job_id parameter to check the chunk progress.) available)
  • Upload preview: Text snippet/PDF 1st page/image thumbnail display
  • Version switcher: dropdown with indexed_at timestamp and Show abbreviated checksum, with hover and copy functions
  • Chunk Preview Button: Tokenize chunks of the selected image or document. Display inline
  • Batch job panel: Allows you to input and cancel multiple files at once
  • Accessibility/Mobile Compatibility: axe-core check and responsive E2E test implementation
  • Dark mode supported: Implemented theme switch
  • visualization.py: Spy cluster plot and energy graph ✅ Implementation completed (frontend/pages/visualization.py)
  • model_management.py: Model management and deployment ✅ Implementation complete (frontend/pages/model_management.py)
  • EvoRAG: Milvus integration and multi-LLM backend support ✅ Implementation complete (evospikenet/rag_milvus.py)
  • Document ingest & versioning: PDF/Word/Excel/PPT/Markdown upload, auto-chunking, history saving ✅
  • File upload/version list/chunk acquisition function via SDK ✅
  • Data creation tab: Add user text and insert vector DB
  • Future plans (2026 Q2): Large capacity (>1GB) streaming import, addition of rich difference view with table/image support
  • Future vision: Cloud brain platform

Phase 12: Backpropagation verification

Implementation date: January 23, 2026 Status: ✅ Fully implemented

A system that comprehensively verifies the accuracy, numerical stability, and convergence of backpropagation in SNN.

Feature list

Features Status Description
GradientVerifier ✅ Gradient verification using finite difference method, validity verification of surrogate gradient
NumericalStabilityTester ✅ NaN/Inf detection, condition number monitoring, gradient/weight norm tracking
ConvergenceAnalyzer ✅ Convergence judgment algorithm, convergence rate calculation, history record
ComparativeBenchmark ✅ SNN vs ANN performance comparison, learning time and accuracy measurement
SurrogateGradient ✅ 5 types of surrogate gradient functions (Fast Sigmoid, Triangular, Rectangular, Exponential, SuperSpike)
BackpropagationVerificationSuite ✅ Integrated verification suite, automatic report generation
Front-end UI ✅ Parameter settings, real-time progress display, 5 analysis tabs, graph visualization, JSON/text export

Technical details

  • Implementation file:
  • evospikenet/backpropagation_verification.py (~750 lines)
  • tests/unit/test_backprop_verification.py (~300 lines, 13 tests)
  • frontend/pages/backprop_verification.py (~750 lines)

  • docs/BACKPROPAGATION_VERIFICATION.md (comprehensive documentation)

  • surrogate gradient function:

  • Fast Sigmoid: \(g(x) = \frac{\beta e^{-\beta x}}{(1 + e^{-\beta x})^2}\)
  • Triangular: \(g(x) = \max(0, 1 - \frac{|x|}{w})\)
  • Rectangular: \(g(x) = \mathbb{1}_{|x| < w}\)
  • Exponential: \(g(x) = \alpha e^{-\alpha |x|}\)

  • SuperSpike: \(g(x) = \frac{\beta}{(\beta |x| + 1)^2}\)

  • Verification items:

  • Accuracy of gradient calculation (comparison with finite difference method: \(\frac{\partial L}{\partial w_i} \approx \frac{L(w_i + \epsilon) - L(w_i - \epsilon)}{2\epsilon}\))
  • Numerical stability (NaN/Inf detection, condition number \(\kappa = \frac{\lambda_{max}}{\lambda_{min}}\))
  • Convergence (Early Stopping, convergence rate \(r = \frac{L_0 - L_T}{T}\))

  • Surrogate gradient validity (comparison of 5 types of function accuracy)

  • UI components:

  • Parameter settings panel (ε, tolerance, convergence threshold, number of iterations)
  • Surrogate gradient function selector
  • Verification mode selection (full verification/gradient only/stability only/convergence only)
  • Real-time progress bar
  • 5 analysis tabs (Summary, Slope Analysis, Numerical Stability, Convergence Analysis, Report)
  • 4 status cards (gradient validation, numerical stability, convergence, execution time)
  • Interactive graphs (gradient error gauge, loss transition, gradient norm transition)
  • JSON/text download function

Phase 13: Complete dummy implementation replacement

Implementation date: January 24, 2026 Status: ✅ Fully implemented

Completely replace dummy implementations, stubs, and placeholders of the entire system with production code. All 21 implementation items have been resolved and the product is ready for production.

Feature list

Categories Features Status Description
Model compression DISTILLATION ✅ Model compression by knowledge distillation, knowledge transfer from teacher model to student model
Vocabulary System VocabularySystem ✅ Basic vocabulary table of 50 vocabulary, tokenization/detokenization function
PFC processor VisionProcessor ✅ Visual information processing, CNN-based feature extraction, attention mechanism integration
PFC processor AudioProcessor ✅ Audio information processing, spectrogram conversion, time series feature extraction
PFC processor LanguageProcessor ✅ Linguistic information processing, Transformer-based context understanding, meaning extraction
PFC processor MotorProcessor ✅ Motion control processing, trajectory planning, feedback control
PFC processor ExecutiveProcessor ✅ Executive control, decision making, task coordination, cognitive control
RAG language model TransformerLM ✅ Transformer-based language model, self-attention mechanism, generative ability
RAG language model SpikingEvoTextLM ✅ Spiking-based evolutionary language model, SNN integration, adaptive learning
Federated learning FlowerIntegration ✅ Flower framework integration, distributed learning collaboration, privacy protection
Alert system MultiChannelAlert ✅ Slack/Email/PagerDuty integration, multi-channel notifications, anomaly detection
Functional module TrajectoryGenerator ✅ Trajectory generation, motion planning, optimal path calculation
Functional module CerebellumPID ✅ Cerebellar PID control, motor coordination, feedback correction
Functional module MotorDriver ✅ Motor driver, execution control, hardware interface
PFC Integration VisualProcessingLoop ✅ Visual Processing PFC Integration, Cognitive Feedback, Adaptive Adjustment
PFC integration AudioProcessingLoop ✅ Audio processing PFC integration, attention control, semantic integration

Technical details

  • Implementation file:
  • evospikenet/model_compressor.py (DISTILLATION implementation)
  • evospikenet/data/vocab_table.json (vocabulary system)
  • evospikenet/pfc.py (5 PFC processors)
  • rag-system/backend/models.py (two language models)
  • evospikenet/api.py (federated learning integration)
  • evospikenet/logging/log_analysis.py (alert system)
  • evospikenet/functional_modules.py (3 functional modules)
  • evospikenet/embodied_pla_loop.py (PFC integration)

  • docs/dummy_mock_implementations.md (comprehensive documentation)

  • Verification results:

  • Completed full functional testing in Docker environment
  • Tensor shape consistency check
  • Fully implemented neural network architecture

  • 21 implementation items 100% complete

  • Quality Assurance:

  • Abstract base class intentionally uses pass statement (for subclass implementation)
  • Concrete implementation is a complete neural architecture
  • Error handling and logging integration
  • Ensure type safety

Phase 14: Detailed specification of future expansion points

Implementation date: January 24, 2026 Status: ✅ Specification completed

With the completion of dummy implementation verification, future expansion points will be specified in detail. Enables cleanup of mock implementations and clarifies implementation plans after Q2.

List of extension points

Extension points Target files Priority Implementation timing
Improved distributed system temporary file management evospikenet/api.py + evospikenet/config_manager.py Low Implemented
High-quality TTS waveform generation frontend/pages/speech_synthesis.py Middle Middle of Q2
Advanced audio-text integration frontend/pages/audio_text_integration.py Medium Late Q2

Technical details

1. Improved distributed system temporary file management - Current: Loads storage directory from configuration file and environment variable ARTIFACT_STORE, defaults to artifacts/files. - Progress: Added path as a configuration in config_manager to start automatic cleaning task when FastAPI starts. By specifying a shared volume, you can share data between Docker containers. - Technical Requirements: Path manipulation with pathlib.Path, configuration management extensions, background cleanup scheduler

2. Implementation of high quality TTS waveform generation - Current status: Basic TTS implementation - Extended specifications: High-quality waveform generation based on WaveGlow/HiFi-GAN, support for multiple speakers, integration of emotional expressions - Technical requirements: PyTorch, torchaudio, mel spectrogram processing

3. Implementing advanced speech-to-text integration - Current status: Basic speech-to-text integration - Extended specifications: Lang-TAS model integration, streaming support, multi-language support, speaker separation - Technical requirements: Transformer, CTC loss, streaming processing

Cleanup policy

  • Basic functionality maintained: The current implementation has been confirmed to work properly.
  • Delete extension: Remove TODO comments and placeholders
  • Specification reference: Added reference to detailed specifications in planned expansion areas.

4. Technical specifications

System requirements

  • Python: 3.9+
  • Hardware: GPU/CPU compatible, integer operation optimization
  • Distributed communication: Zenoh PubSub, PTP time synchronization
  • Dependencies: PyTorch, NumPy, various SNN libraries

Performance metrics (after Feature 13 integration)

  • Energy efficiency: More than 100 times (compared to ANN), additional 50% reduction with spatial processing module
  • End-to-end processing: 150-200ms (including multisensory integration, spatial-cognitive loop)
  • Real-time processing: 30+ fps (HD images), spike sparsity 5-15%
  • Scalability: Supports 100+ distributed nodes, <5ms synchronization error in 24 node environment
  • Spatial cognitive performance:
  • Where processing: <50ms, depth estimation accuracy 90%+
  • What processing: <30ms, object recognition 85-92% (ImageNet-100)
  • Integration: <50ms, world model resolution 256³ voxels
  • Attention control: <30ms, saccade accuracy ±3 degrees

Security/Quality

  • Static analysis: Black, isort, Flake8, Pylint, mypy, Bandit
  • CI/CD: GitHub Actions integration
  • Code quality: Pylint ≥7.0, Docstring coverage ≥60%
  • Test: 17+ Spatial Processing Tests, 100% Success Rate, Comprehensive Integration Test

5. Conclusion

EvoSpikeNet is the world's first implementation of a distributed brain architecture as the ultimate biological spiking neural network. The Q-PFC Loop, spike distillation, cognitive load feedback, and Feature 13 Spatial Perception and Generation System combine to create a revolutionary platform that delivers human-like spatial understanding, multitasking, and energy efficiency.

The integration of spatial processing nodes (Rank 12-15) enables complete multisensory integration of visual-spatial-verbal-motor, enabling innovation in diverse application areas such as robot control, autonomous driving, and multimodal AI.

**As of March 2026: Phase E-0/E-1/E-2 connectome integration has been completed (2026-03-19). ** The infrastructure (ConnectomeLIFLayer, SparseDelayBuffer, ConnectomeMetadataPublisher) that injects real-world neural circuit data of C. elegans, FlyWire, MICrONS, and HCP into nodes is up and running, and 102 tests have passed. This provides a foundation for spontaneous circuit formation, structurally constrained STDP, and Zenoh routing optimization based on biological topology.

Through gradual evolution from Phases 1 to 11, this framework goes beyond mere neuromorphic computing to realize true brain function simulation. Plug-in architecture and microservices greatly improve development efficiency and scalability, and provide an open ecosystem that is easily accessible to researchers and companies.

EvoSpikeNet is the key to unlocking the future of AI and will revolutionize a wide variety of applications, from quantum brain edge devices to cloud brain platforms.