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Implements a five-layer bio-inspired nervous system for RuVector with: ## Core Layers - Event Sensing: DVS-style event bus with lock-free queues, sharding, backpressure - Reflex: K-Winner-Take-All competition, dendritic coincidence detection - Memory: Modern Hopfield networks, hyperdimensional computing (HDC) - Learning: BTSP one-shot, E-prop online learning, EWC consolidation - Coherence: Oscillatory routing, predictive coding, global workspace ## Key Components (22,961 lines) - HDC: 10,000-bit hypervectors with XOR binding, Hamming similarity - Hopfield: Exponential capacity 2^(d/2), transformer-equivalent attention - WTA/K-WTA: <1μs winner selection for 1000 neurons - Pattern Separation: Dentate gyrus-inspired sparse encoding (2-5% sparsity) - Dendrite: NMDA coincidence detection, plateau potentials - BTSP: Seconds-scale eligibility traces for one-shot learning - E-prop: O(1) memory per synapse, 1000+ms credit assignment - EWC: Fisher information diagonal for forgetting prevention - Routing: Kuramoto oscillators, 90-99% bandwidth reduction - Workspace: 4-7 item capacity per Miller's law ## Performance Targets - Reflex latency: <100μs (Cognitum tiles) - Hopfield retrieval: <1ms - HDC similarity: <100ns via SIMD popcount - Event throughput: 10,000+ events/ms ## Deployment Mapping - Phase 1: RuVector foundation (HDC + Hopfield) - Phase 2: Cognitum reflex tier - Phase 3: Online learning + coherence routing ## Test Coverage - 313 tests passing - Comprehensive benchmarks (latency, memory, throughput) - Quality metrics (recall, capacity, collision rate) References: iniVation DVS, Dendrify, Modern Hopfield (Ramsauer 2020), BTSP (Bittner 2017), E-prop (Bellec 2020), EWC (Kirkpatrick 2017), Communication Through Coherence (Fries 2015), Global Workspace (Baars) |
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| .. | ||
| btsp_usage.rs | ||
| monitoring_example.md | ||
| sparsevec_examples.sql | ||