Six neural network types. Biological plasticity. Developmental learning. Non-removable safety. Running on a single GPU.
NIMCP doesn't train a single monolithic model. It trains six heterogeneous neural networks simultaneously, with gradient flow across network boundaries.
Five learning rules at five timescales: STDP (10ms), BCM (50ms), eligibility traces (1s), structural plasticity (10s), homeostatic scaling (60s). Four neuromodulators modulate learning from reward and novelty signals.
Four stages mirroring human cognitive development: sensory awakening, cross-modal naming, feedback learning, abstract reasoning. Each stage builds on the previous one.
Ethics module is always created regardless of configuration. LGSS governance evaluates every inference and weight update. Tamper-resistant audit log. Safety rules can only get stricter.
12 sensor types, safety watchdog, motor output. Four drone interfaces. ROS 2 bridge. Sim-to-real transfer with domain randomization.
Multi-device federation with UDP discovery, Byzantine fault tolerance, and gossip-based gradient aggregation. Theory of Mind through multi-agent observation.
Learned vocabulary from neural activation patterns. Autoregressive decoding with nucleus sampling. Emergent omega-tokens. Inner speech loop for self-refinement.
Detailed documentation of the mathematics, training methodology, and broader implications of NIMCP.
Complete mathematical framework: LIF dynamics, LNN ODEs, adjoint gradients, STDP/BCM learning rules, Fourier spectral methods, Hamiltonian mechanics, information geometry, and safety mathematics. Every equation corresponds to implemented code with source file references.
2,298 LINES • 40 SECTIONS • EQUATIONS + CODE REFSHow NIMCP trains six networks simultaneously through a four-stage developmental curriculum. Systematic comparison with conventional deep learning and transformer training across 12 dimensions including data efficiency, continual learning, and safety integration.
446 LINES • COMPARATIVE ANALYSISAnalysis of ubiquitous NIMCP deployment across healthcare, education, manufacturing, agriculture, environmental monitoring, disaster response, and governance.
366 LINES • IMPACT ASSESSMENTHow biologically realistic firing patterns (26 Hz, 67% sparsity) arise from BPTT training in a six-network brain without explicit regularization.
EMPIRICAL RESULTS • NEUROSCIENCEStructural safety guarantees vs. behavioral training. Nine-layer governance system with non-removable ethics, monotonic rules, tamper-resistant audit. Compared to RLHF and Constitutional AI.
AI SAFETY • FORMAL VERIFICATIONLearnable bridges enable knowledge transfer between spiking, liquid, and rate-coded networks. Composite loss with contrastive pressure drives representational specialization.
NOVEL ARCHITECTURE • GRADIENT ANALYSISPrediction error drives dopamine-gated STDP in a closed sensorimotor loop. No external reward function. Natural exploration-exploitation transition. ROS 2 robot deployment.
INTRINSIC MOTIVATION • EMBODIED AIBig Five personality traits modulate neuromodulator baselines, shaping how the brain learns. Emotional state drives voice prosody. Identity is a learning parameter, not a style overlay.
PERSONALITY • AFFECTIVE COMPUTINGA 2.5-million neuron brain named Athena is currently in Stage 2 of developmental training on a single NVIDIA RTX 4000 SFF Ada (20 GB VRAM).
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NIMCP compiles on any Linux system with a CUDA-capable GPU.