I've spent the last year building NIMCP — a biologically-inspired artificial brain in C that trains six different neural network types simultaneously (spiking, liquid, convolutional, Fourier, Hamiltonian, adaptive) with gradient flow between them through learnable bridges.
Some things that might be interesting to this crowd:
- The SNN developed 26 Hz firing rates with 67% sparsity — within mammalian cortical range — without any regularization targeting those values. It emerged from cross-network training
pressure.
- Safety is structural, not behavioral. The ethics module is a function call in the inference code path, not a learned weight. It can't be fine-tuned away or jailbroken. The governance rules can only get stricter. You can verify this by reading the source.
- The brain learns through curiosity: prediction error → dopamine → STDP gating. No reward function.
- Training follows a 4-stage developmental curriculum (sensory → naming → feedback →reasoning). The training is currently in Stage 2. You can watch it train live on the website — metrics update every 60 seconds.
- 2,600 source files, 240 Python API methods, 8 language bindings. The system runs on a single RTX 4000 (20 GB VRAM).
Eight technical papers on the site covering the math, training methodology, safety architecture, and emergent dynamics.
Code: https://github.com/redmage123/nimcp
I am happy to answer questions about the architecture, training dynamics, or why I think growing intelligence through developmental stages might work differently than scaling transformers.
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