From a medical professional with 30 years in patient care
who spent years simulating biological systems in C++
From a virtual heart that beat with 12 leads of electrical data,
responding to drugs and modeled conduction abnormalities... to biomimetic robotic fingers and hands...
He was always building toward an electrical mind. He just didn't know it yet.
Intelligence that grows.
A new kind of AI — evolved from random noise, trained without backpropagation, capable of learning continuously after deployment. Not an incremental improvement to existing models. A fundamentally different path toward artificial general intelligence.
Patent Pending
Every biological mind on Earth — every human, every octopus, every crow that bends wire into a hook — arrived at general intelligence the same way: 3.8 billion years of evolution. Random mutation. Selection pressure. No architect. No blueprint. Just relentless competition and the ones that worked got to keep going.
We can run that same process in silicon — compressed from eons into right now. Start with random bytes. Apply selection pressure: predict human language, or be replaced. Distinct computational species emerge from noise. They organize into layered circuits with reward, punishment, and permanent memory.
Every AI system in production today was designed by humans, trained by gradient descent, and frozen at deployment. It can never learn another thing. The Evolved Neural Circuit takes the other path — the one that actually produced intelligence the first time around. The result is a system with properties that no transformer possesses: online learning, neuroplasticity, biological reward mechanisms, and the ability to grow with every interaction. It learns the way brains learn. And it never stops.
This is early-stage technology with demonstrated results. A working circuit — evolved from pure noise, organized into layers, trained with virtual dopamine and pain and other proprietary methods — that generalizes to unseen data. Not a simulation of intelligence. A prototype of something new.
50+ independent evolutionary runs. Thousands of species emerged from random bytes. Three champions selected through competition. No human designed these neurons — nature did.
Multiple neurotransmitter channels modulate learning, attention, and behavior — the same chemical vocabulary the brain uses. Reward and punishment aren't bolted on. They're structural.
The entire architecture is designed for CPU cache hierarchies, not GPU tensor cores. Each layer fits in L1 cache (128 KB). Each functional region fits in L2 (4 MB). The brain grows by adding regions — limited only by available RAM. Irregular branching per unit, pointer chasing across layers, conditional tape execution — all things CPUs excel at and GPUs choke on. Runs on any device with a CPU. Learn more →
Tested on data the circuit has never seen. Performance on unseen data matches or exceeds training accuracy — consistently, at every checkpoint. This is not memorization. The circuit learns transferable structure.
Load specialized knowledge — medicine, law, coding — on demand. Bridge columns connect skills to the core circuit bidirectionally. LRU eviction preserves session learning. One deep skill can claim the entire budget.
The circuit actively prunes permanent connections that become wrong and replaces them with better ones. Connections proven by 8+ concurrent solves get myelinated — frozen insulated highways that carry signal at full strength. Self-correction without external intervention.
No gradients. No loss functions. No optimizer states. Dopamine rewards connections that contribute. Pain weakens connections that harm. Evolution selects the computational species that survive. The circuit grows the way brains grow.
A programmatic hippocampus records every experience — tagging each with reward, novelty, and confidence. During training it takes notes. During dream consolidation it replays the most important memories, strengthening verified pathways. The circuit learns while awake and consolidates while it sleeps — just like a brain.
250,000 real English words from GloVe 840B — not subword fragments. Every token is a word you can read. Contraction splitting, camelCase handling, and letter-by-letter fallback achieve 0% unknown rate on English text. 40 million tokens from 6 datasets tokenized and ready for training.
Eight parallel boxes — 12 layers each, 2,048 units per layer, 4 MB per functional region — trained from scratch on CPU. No pretrained weights. No transfer learning. 1,024-dimensional embeddings, 192 tokens of context, 80 tokens/second inference. Starting from random noise, reaching meaningful accuracy in seconds.
Trained and validated with train/test split on an Apple M4 Pro. 8 parallel boxes on 8 CPU cores. No GPU. No cluster. 40 million tokens of training data from 6 diverse datasets, tokenized with a 250,000-word human-readable vocabulary.
The circuit's proprietary verification mechanism identifies exactly which connections are responsible for correct predictions — something impossible in conventional neural networks where knowledge is distributed across billions of opaque weights. Only verified connections earn permanence. Everything else stays fluid, available for continued learning.
Fast. A trained circuit runs faster than an untrained one — pruning and pain carve away weak connections, leaving sparser activation patterns that execute more efficiently. The circuit gets faster as it gets smarter.
At scale, the filing cabinet architecture means only active regions execute. A brain with thousands of functional regions but three active ones runs at the same speed as a three-region brain. Dormant knowledge costs nothing until it's needed.
A well-trained transformer LLM evaluated the same way — ranking the correct token against 1,000 random candidates — would score approximately 95–99% acc@1. The ENC at 52% has a real gap to close.
But consider what's being compared: a 4 MB circuit trained on CPU, versus models trained on trillions of tokens across thousands of GPUs over months. The ENC uses no backpropagation, no gradients, no optimizer states. It learns from scratch — from random noise to meaningful accuracy in seconds, to 52% with biological learning mechanisms alone.
| Transformer LLM | Evolved Neural Circuit | |
|---|---|---|
| acc@1 (vs 1K candidates) | ~95–99% | 52% |
| Inference speed | ~30–100 tok/s (consumer GPU) | 80 tok/s, CPU-native, no GPU |
| Model size | 4–70 GB | Scales as needed |
| Training time | Weeks on GPU clusters | Hours on CPU |
| Context window | 4K–128K tokens | 192 tokens (8 parallel boxes × 24) |
| Training data | Trillions of tokens | 40M tokens |
| Learns after training | No — weights can only be fine-tuned after creation | Infere only or learn continuously, in real time |
The question is not whether the ENC matches a transformer today. It does not. The question is whether this architecture — with its learning speed, storage efficiency, continuous adaptation, and a 40-million-token training pipeline ready to scale — can close the gap. The curve has room to climb.
40 million tokens from 6 diverse datasets, tokenized with a 250,000-word human-readable vocabulary. Parallel functional regions — 8 boxes per region, each on its own CPU core — train with the full neuromodulation stack. The pipeline is built. Now it scales.
The circuit doesn't care what the input embeddings represent. Feed it image patches alongside text tokens and it learns cross-modal associations through the same biological mechanisms. One architecture for all senses.
The brain handles short-term reasoning; confirmed prediction neuron traces are released to long-term memory (O(1) hash lookup, nanosecond retrieval). The brain stays plastic indefinitely — solved problems free capacity for new learning. The more it trains, the faster it gets.
The circuit detects when it's plateauing, saves its hippocampal journal, enters dream mode to consolidate, and wakes up better. Train-dream-train cycles that run autonomously. The programmatic hippocampus learns to become a neural one — routing by experience, not rules.
Every other AI architecture is built on matrix multiplication — dense linear algebra that maps perfectly to GPU tensor cores. Without a GPU, they crawl. The Evolved Neural Circuit has no weight matrices. No matrix multiplies. No dense linear algebra at all.
Each computational unit runs a 16-byte tape program with branching, pointer chasing, and conditional logic. This is exactly what CPUs are built for — and exactly what GPUs are bad at. Thread divergence, random memory access, irregular control flow: these destroy GPU utilization but thrive on CPU cache hierarchies and branch prediction.
Zero GEMMs. Zero tensor core operations. The thing GPUs are specifically designed to accelerate doesn't exist in this architecture.
Each of 2,048 units per layer runs its own tape with unique control flow. CPUs handle this per core. GPUs force lockstep execution — one divergent branch stalls the entire warp.
Each layer is exactly L1 cache size on Apple Silicon (128 KB). The entire working set is always in the fastest memory on the chip. No cache misses. No memory stalls.
8 independent boxes run on 8 CPU cores via GCD dispatch. Each processes its own context slice. No synchronization during forward pass. Clean scaling with core count.
A transformer needs a datacenter GPU and hundreds of watts to think. The Evolved Neural Circuit needs a cache line and milliwatts. The brain has no maximum size — it grows by adding functional regions, the way the human cerebral cortex is organized into 180 distinct areas per hemisphere. The hippocampus routes attention to the right regions and adds new ones as the brain learns. Each region is as small as 4 MB, fits in L2 cache, and runs on its own CPU core. The brain is limited only by available RAM — with mechanisms to swap specialized knowledge from disk when it exceeds that. It runs at full speed on a phone, a Raspberry Pi, a medical device, a satellite — anywhere there's a CPU. No cloud. No API call. No GPU allocation queue. The entire history of deep learning has been a story of scaling GPU compute. This architecture asks a different question: what if AI was designed for the processor that's already in every device on Earth?
| Transformer LLMs | Evolved Neural Circuit | |
|---|---|---|
| Origin | Human-designed architecture | Evolved from random bytes |
| Training | Backpropagation on GPU clusters | Evolution + biological selection |
| Learns after deployment | No — weights can only be fine-tuned after creation | Continuously, in real time |
| Self-corrects | No | Neuroplasticity — prunes and rewires |
| Knows what it doesn't know | No — guesses confidently | Yes — uncertainty is structural |
| Hardware | Datacenter GPUs, megawatts | CPU-native, milliwatts |
| Modular expertise | Monolithic — retrain everything | Hot-swap skill slots on demand |
| Interpretable | Black box — billions of opaque weights | Glass box — every connection traceable |
| Multimodal by design | Bolted on after the fact | Architecture-agnostic input — any embedding |