Morse Code Recognition
Initial implementation. 5-neuron canonical circuit based on the Münster model.
- 5-neuron circuit (AN1, LN2, LN3, LN5, ON1)
- Basic Morse code encoding/decoding
- Criterion benchmarks (0.175 µs/step)
- 0 false positives during silence
v3.0.0 — Production Release
CricketBrain
Adaptive neuromorphic signal processing. Hardwired core + optional STDP plasticity. Inspired by 200 million years of cricket evolution.
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Neurons
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Bytes RAM
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GPU Required
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Tests Passing
Live Simulation
Watch the 5-neuron Münster circuit process signals in real-time. ON1 fires only when it detects sustained 4500 Hz via delay-line coincidence detection.
Input Silence
AN1 Amp 0.000
ON1 Out 0.000
Spikes 0
Timestep 0
Press Play to start. Use presets or the frequency slider to feed signals into the 5-neuron network. Watch how only 4500 Hz triggers resonance.
Neural Architecture
Five neurons connected by six delay synapses. Inhibitory paths (red) suppress noise, the excitatory path (green) drives the output.
Mathematical Foundation
The hardwired core is built on four equations. Optional STDP plasticity enables online weight adaptation.
Gaussian Tuning
match = e-(Δf / f₀ / w)²
Frequency selectivity. w = 0.1 gives ±10% bandwidth. At 20% deviation, match < 0.02.
Amplitude Update
A(t+1) = min(A + m·0.3, 1.0)
When resonating: amplitude grows proportionally to match strength, capped at 1.0.
Phase Locking
φ(t+1) = φ + (φin - φ)·0.1
Exponential moving average locks the neuron's phase to the input signal.
Coincidence Detection
fire = (A > θ) ∧ (At-τ > θ·0.8)
Output fires only when BOTH current and delayed amplitude exceed threshold. Prevents false positives.
How It Compares
CricketBrain is not competing with deep learning. It proves that useful inference is possible at a fundamentally different scale.
| Property | CricketBrain | Deep Learning |
|---|---|---|
| Latency | 0.175 µs/step | ~10 ms |
| Memory | ~1 KB | ~800 GB |
| Training | Optional STDP | Months on cluster |
| GPU required | No | Yes |
| Deterministic | Yes | No |
| no_std / Embedded | Yes | No |
| Explainable | Fully | Black box |
| False positives in silence | 0 | N/A |
Version History
Multi-Frequency Tokens
Multi-frequency token vocabulary and parallel resonator banks.
- 27-token alphabet (A-Z + space)
- Resonator bank: one 5-neuron circuit per token
- Live demo with encode-brain-decode roundtrip
- Frequency discrimination example
Sequence Prediction
Temporal pattern matching with confidence scoring and privacy mode.
- N-gram pattern matching engine
- Confidence: C = SNR/(1+SNR) · (1 - jitter/tolerance)
- Privacy mode for HIPAA/GDPR compliance
- Dual licensing (MIT OR Apache-2.0)
Production Release
Full workspace with cross-platform bindings, STDP plasticity, and research-grade benchmarks.
- C/FFI, Python (PyO3), WASM bindings
- STDP + homeostatic plasticity
- no_std core with
#![deny(unsafe_code)] - License: AGPL-3.0 + Commercial
Quick Start
Terminal
# Clone and run
git clone https://github.com/BEKO2210/cricket-brain.git
cd cricket-brain
cargo run
# Full roundtrip demo
cargo run --example live_demo -- "HELLO WORLD"
# Sequence prediction
cargo run --example sequence_predict
# 40k neuron benchmark
cargo run --release --example scale_test
# Run all 122 tests
cargo test --workspace