Adaptive neuromorphic signal processing. Sub-kilobyte memory. 97 ns/step.

Hardwired core + adaptive plasticity. Inspired by 200 million years of cricket evolution.

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What is CricketBrain?

CricketBrain is a neuromorphic signal processor that recognizes temporal patterns in real-time using delay-line coincidence detection — the same mechanism the field cricket (Gryllus bimaculatus) uses to find mates in noisy environments.

Hardwired core architecture with optional adaptive plasticity. 5 neurons, 6 synapses, STDP learning, homeostatic regulation — 97 ns/step in ~1 KB RAM.

Why Should You Care?

	CricketBrain	Traditional ML	Deep Learning
Latency	97 ns	~100 us	~10 ms
Memory	~1 KB	10+ KB	100+ MB
Training	Optional STDP	Hours	Days-Weeks
GPU	No	No	Yes
Deterministic	Yes	Depends	No
no_std / Embedded	Yes	Rare	No
Explainable	Fully	Partially	Black box

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Use Cases

10 detailed use cases with market analysis: USE_CASES.md

Domain	Application	How CricketBrain Helps
Medical Research	ECG rhythm analysis	Temporal pattern detection for rhythm classification research (demo)
Industrial IoT	Vibration monitoring	Detect bearing failure patterns at the sensor node
Audio	Keyword / wake-word detection	Sub-millisecond response without cloud roundtrip
Security	Network traffic analysis	Temporal pattern anomaly detection at line rate
Robotics	Sensor fusion	Deterministic latency for real-time control loops
Embedded	Microcontroller signal processing	Runs on Arduino Uno (2 KB RAM)

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Quick Start

Install & Run (30 Seconds)

git clone https://github.com/BEKO2210/cricket-brain.git
cd cricket-brain
cargo run --example live_demo -- "HELLO WORLD"

--- Spike Train (each char = 10ms) ---
|||||_____|||||_____|||||_____|||||_______________|||||_______________...

Decoded output: "HELLO WORLD"
Match: EXACT MATCH

Use as a Library

[dependencies]
cricket-brain = "3.0"

use cricket_brain::prelude::*;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut brain = CricketBrain::new(BrainConfig::default())?;

    // Feed a 4500 Hz signal — spikes appear
    for _ in 0..100 {
        let output = brain.step(4500.0);
        if output > 0.0 {
            println!("Spike! amplitude={output:.3}");
        }
    }

    // Silence — guaranteed zero false positives
    for _ in 0..50 {
        assert_eq!(brain.step(0.0), 0.0);
    }

    brain.reset();
    Ok(())
}

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Multi-Language Support

Rust (native) use cricket_brain::prelude::*; let mut brain = CricketBrain::new( BrainConfig::default() )?; let out = brain.step(4500.0);	C / C++ / Swift BrainHandle *h = NULL; brain_new(&h, 5, 4000.0, 5000.0); float out; brain_step(h, 4500.0, &out); brain_free(h);
Python from cricket_brain import BrainConfig, Brain brain = Brain(BrainConfig()) out = brain.step(4500.0) batch = brain.step_batch([4500.0] * 100)	JavaScript / TypeScript (WASM) import { Brain } from "cricket-brain-wasm"; const brain = new Brain(42); const out = brain.step(4500.0); const events = brain.drainTelemetry();

cargo build --release -p cricket-brain-ffi        # C FFI  →  crates/ffi/include/cricket_brain.h
cd crates/python && maturin develop --release      # Python →  pip install cricket-brain
cd crates/wasm && wasm-pack build --target web     # WASM   →  npm package

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Verified Results

The following results were reproduced locally on Windows using the public examples and benchmark commands in this repository.

Local verification highlights

Scenario	Result	What it shows
Live roundtrip	"HELLO WORLD" → EXACT MATCH	End-to-end encoding, spike processing, and decoding work correctly
Frequency discrimination	Strong response at 4200–4800 Hz, peak at 4500 Hz	Tight frequency selectivity around the cricket carrier band
Morse alphabet	All A–Z produce stable spike signatures	Reliable temporal-symbol encoding
Multi-frequency tokens	"RUST" detected at 100% accuracy	Parallel token discrimination across frequency bands
Sequence prediction	Correct prefix-based next-token prediction	Topology-based temporal memory without gradient training
Scale predictor	1,280 neurons, 0.31 MB RAM, 1.12e8 neuron-ops/sec	Efficient scaling for structured sequence tasks
Large scale test	40,960 neurons, 14.22 MB RAM, 8.02e7 neuron-ops/sec	High throughput on commodity CPU hardware
ECG sentinel demo	0.141615 us/step, performance gate PASS	Real-time suitability for edge monitoring workloads
Latency profile	0.103475 us/step baseline	Extremely low per-step overhead in optimized mode

Classical baseline comparison

Under the included synthetic benchmark suite, CricketBrain achieved:

• TPR = 1.000
• FPR = 0.000
• across all tested SNR levels from -10 dB to +30 dB

Compared methods:

• Matched Filter
• Goertzel
• IIR Bandpass
• CricketBrain

This indicates that CricketBrain is especially strong on noise-robust temporal pattern detection in the benchmark conditions defined by this repository.

Circuit ablation findings

The ablation study shows an important architectural result:

• LN3 removal causes a major performance collapse
• LN2 and LN5 removal had little effect in the tested setup
• delay-line / coincidence-related ablations did not reduce performance in the same benchmark regime

This suggests:

• LN3 is currently the dominant functional component in the tested task
• some biologically inspired mechanisms may require harder or more targeted benchmarks to fully demonstrate their contribution

Interpretation

These results support the claim that CricketBrain is:

• fast
• memory-efficient
• deterministic
• effective for temporal pattern recognition on edge-class hardware

They do not yet prove universal superiority over all classical or learned methods on all datasets.
What they do show is that CricketBrain is a serious and reproducible neuromorphic signal-processing architecture with unusually strong efficiency and very promising benchmark behavior.

Reproduce locally

cargo run --example live_demo -- "HELLO WORLD"
cargo run --example frequency_discrimination
cargo run --example morse_alphabet
cargo run --example multi_freq_demo -- "RUST"
cargo run --example sequence_predict
cargo run --release --example scale_predict
cargo run --release --example sentinel_ecg_monitor
cargo run --release --example baselines
cargo run --release --example ablation_study
cargo run --release --example research_gen -- --seed 1337
cargo run --release --example scale_test
cargo run --release --example profile_speed
cargo bench

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Benchmarks

Scenario	Latency	Throughput	Memory
Canonical 5-neuron	0.175 us/step	10.7M steps/sec	928 bytes
1,280-neuron predictor	—	50.1M neuron-ops/sec	0.30 MB
40,960-neuron scale	—	40.7M neuron-ops/sec	13.91 MB
Arduino no_std	—	—	928 bytes

vs. Classical Baselines (SNR = 0 dB)

Tested against 3 classical detectors under identical conditions (source):

Method	TPR	FPR
CricketBrain	1.000	0.000	Temporal coincidence rejects noise
IIR Bandpass	1.000	0.558	Cannot distinguish pattern from noise
Goertzel (FFT)	0.017	0.000	Misses jittered signals
Matched Filter	0.000	0.000	Needs high SNR (>10 dB)

CricketBrain achieves perfect detection (TPR=1.0) with zero false positives (FPR=0.0) across all SNR levels from -10 dB to +30 dB.

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Features

Core

• Gaussian resonators — frequency-selective neurons with adaptive bandwidth
• Delay-line synapses — ring-buffer propagation delays (1-9 ms)
• Coincidence detection — fires only on sustained temporal evidence
• Adaptive sensitivity (AGC) — automatic gain control
• Synaptic plasticity (STDP) — online weight adaptation via spike-timing
• Homeostatic thresholds — automatic target activity maintenance
• Sequence prediction — N-gram pattern matching with confidence scoring
• Multi-token detection — parallel resonator banks (one circuit per token)

Production

Feature	Description
Privacy Mode	Timestamp anonymization + value coarsening (designed for privacy-sensitive contexts)
Snapshot/Restore	Serialize full state with FNV-1a checksums
Telemetry	Structured event hooks + JSON Lines sink
Chaos Detection	Shannon entropy monitoring with overload alerts
Deterministic	Seeded RNG — bitwise identical results across platforms
Error Codes	Consistent FFI contract across Rust/C/Python/WASM

Feature Flags

cricket-brain = { version = "1.0", features = ["serde", "parallel"] }

Flag	Default	Description
std	Yes	Standard library support
no_std	—	Embedded mode (alloc only)
serde	—	Snapshot serialization with FNV-1a checksums
parallel	—	Rayon-based resonator bank parallelism
telemetry	—	Structured event hooks
cli	—	JSON telemetry sink + config parsing

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Embedded / no_std

The core crate is fully no_std compatible with #![deny(unsafe_code)].

cargo build -p cricket-brain-core --no-default-features

Minimal embedded example (examples/arduino_minimal.rs):

• Fixed-size arrays (no heap allocation)
• 928 bytes calculated RAM footprint
• Designed for Arduino Uno, STM32, ESP32, any Cortex-M (host-verified)

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Scientific Validation

Artifact	Description
RESEARCH_WHITEPAPER.md	Full paper with 16 peer-reviewed references
examples/baselines.rs	Matched filter, Goertzel, IIR bandpass comparison
examples/ablation_study.rs	Systematic circuit component analysis
examples/research_gen.rs	SNR sweep with Wilson 95% confidence intervals
benchmarks/stress_test_benchmark.rs	Adversarial stress test (10 seeds, pink noise, in-band interferers)
USE_CASES.md	10 real-world applications with market analysis
AI_DEVELOPMENT_STATEMENT.md	Full AI-tooling transparency disclosure

Ablation Study

Configuration	SNR 0 dB TPR	Finding
Full circuit	1.000	Baseline
Without LN3 (excitatory)	0.440	Critical — main excitatory drive
Without LN2 (inhibitory)	1.000	Redundant at this SNR
Without coincidence gate	1.000	Gate essential at low SNR

cargo run --release --example baselines         # Classical comparison
cargo run --release --example ablation_study    # Component analysis
cargo run --release --example research_gen -- --seed 1337  # Full SNR sweep
cargo run --release --example bench_stress      # Adversarial stress test

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Examples

# Basics
cargo run --example live_demo -- "HELLO"       # Encode -> brain -> decode roundtrip
cargo run --example frequency_discrimination   # Gaussian bandpass visualization
cargo run --example morse_alphabet             # All 26 characters
cargo run --example arduino_minimal            # no_std embedded demo

# Multi-frequency tokens
cargo run --example multi_freq_demo -- "RUST"  # 100% token discrimination

# Sequence prediction
cargo run --example sequence_predict           # "hello" vs "help" disambiguation
cargo run --release --example scale_predict    # 256 tokens, 1280 neurons

# Medical / research
cargo run --release --example sentinel_ecg_monitor  # ECG tachycardia detection
cargo run --release --example baselines             # Classical baseline comparison
cargo run --release --example ablation_study        # Circuit ablation study

# Performance
cargo run --release --example scale_test       # 40,960-neuron throughput
cargo run --release --example profile_speed    # Latency measurement
cargo bench                                    # Criterion benchmarks

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Quality

Check	Status
cargo test --workspace	122 tests passing
cargo clippy -D warnings	Zero warnings
cargo fmt -- --check	Enforced
cargo audit --deny warnings	Zero vulnerabilities
Cross-platform CI	Linux, macOS, Windows
no_std verification	Verified in CI
WASM build	Verified in CI
FFI header sync	Verified in CI
CodeQL security scan	Rust + Actions

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Project Structure

cricket-brain/
|-- crates/
|   |-- core/          no_std primitives (neuron, synapse, telemetry)
|   |-- ffi/           C-compatible API + generated header
|   |-- python/        PyO3 bindings
|   `-- wasm/          wasm-bindgen bindings
|-- src/               Brain network, sequence predictor, resonator bank
|-- examples/          14 runnable examples + Python + WASM demo
|-- tests/             122 tests (unit, integration, edge-case, FFI, plasticity)
|-- benches/           Criterion benchmarks
`-- docs/              Mathematical derivations

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Roadmap

• v0.1 — Morse code recognition
• v0.2 — Multi-frequency token recognition
• v0.3 — Sequence prediction via delay-line pattern memory
• v1.0 — Production release with FFI/Python/WASM bindings
• v2.0 — Adaptive Gaussian bandwidth for dense vocabularies
• v3.0 — STDP + homeostatic plasticity for online adaptive learning
• v4.0 — Hardware deployment on RISC-V / ARM Cortex-M with real-time ADC

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

cargo test --workspace                                    # All tests pass
cargo clippy --all-targets --all-features -- -D warnings  # Zero warnings
cargo fmt -- --check                                      # Formatting clean

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Citation

@software{aslani2026cricketbrain,
  author  = {Aslani, Belkis},
  title   = {CricketBrain: A Biomorphic Delay-Line Coincidence Detector
             for Real-Time Temporal Pattern Recognition},
  year    = {2026},
  url     = {https://github.com/BEKO2210/cricket-brain},
  version = {3.0.0}
}

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License

Dual-licensed:

Use Case	License	Cost
Open-source, research, education	AGPL-3.0	Free
Proprietary / commercial / SaaS / embedded	Commercial License	Paid

The AGPL-3.0 requires that any software using CricketBrain must also be released under AGPL-3.0 (including source code). If you cannot comply, you need a commercial license.

Contact: belkis.aslani@gmail.com

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Auf Deutsch

Deutsche Zusammenfassung (klicken zum Aufklappen)

Was ist CricketBrain?

CricketBrain ist ein neuromorpher Signalprozessor, inspiriert vom Hoersystem der Feldgrille (Gryllus bimaculatus). Er erkennt zeitliche Muster in Echtzeit mit Verzoegerungsleitungs-Koinzidenzdetektion.

Fest verdrahteter Kern mit optionaler adaptiver Plastizitaet: STDP-Lernen, gewichtete Synapsen, homeostatische Schwellenregelung.

Kernzahlen

Metrik	Wert
Latenz	97 ns pro Schritt
Speicher	~1 KB (no_std, Embedded-kompatibel)
Erkennung	TPR 1,0 / FPR 0,0 ueber alle SNR-Stufen
Neuronen	5 (Muenster-Modell: AN1, LN2, LN3, LN5, ON1)
Training	Keins initial — optionales STDP Online-Lernen

Anwendungsbereiche

• Medizinforschung: EKG-Rhythmus-Analyse fuer Forschungszwecke (Privacy-Features integriert)
• Industrie 4.0: Vorausschauende Wartung direkt am Sensor-Knoten
• Audio: Wake-Word-Erkennung unter 1 ms ohne Cloud
• Sicherheit: Netzwerk-Anomalie-Erkennung in Leitungsgeschwindigkeit
• Embedded: Laeuft auf Arduino Uno (2 KB RAM)

Schnellstart

git clone https://github.com/BEKO2210/cricket-brain.git
cd cricket-brain
cargo run --example live_demo -- "HALLO WELT"

Sprach-Bindings

Sprache	Befehl
Rust	cricket-brain = "3.0" in Cargo.toml
C/C++	cargo build -p cricket-brain-ffi (Header: cricket_brain.h)
Python	cd crates/python && maturin develop
WASM	cd crates/wasm && wasm-pack build --target web

Lizenz

Dual-lizenziert: AGPL-3.0 (kostenlos fuer Open Source) oder kommerzielle Lizenz (fuer proprietaere Software). Details in COMMERCIAL.md.

Wissenschaftliche Validierung

Das Projekt enthaelt ein vollstaendiges Publikationspaket mit 16 Fachreferenzen, Baseline-Vergleichen (Matched Filter, Goertzel, IIR), einer Ablationsstudie und Wilson-95%-Konfidenzintervallen. Siehe RESEARCH_WHITEPAPER.md.

Autor

Belkis Aslani — entwickelt mit KI-Unterstuetzung (Claude Code, ChatGPT/Codex, Kimi, Gemini). Siehe AI_DEVELOPMENT_STATEMENT.md.

Kontakt: belkis.aslani@gmail.com

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Author: Belkis Aslani

Built with AI-assisted development (statement) using Claude Code, ChatGPT/Codex, Kimi, and Gemini.

GitHub | Docs | Whitepaper | Changelog