🧬 Biocomputing Research

Wetware Computing

Biological-digital hybrid architectures for adaptive intelligence

94.4%
Energy Reduction
6.4×
Lifetime Extension
96.1%
ECG Detection F1
01

Abeone Wetware Core

Four-layer architecture combining organic substrates with digital processing

🧫

Biological Substrate Layer

Neuronal arrays, organic sensors, DNA storage with natural nonlinearity and adaptation

10⁻¹⁸ J/op Energy per operation

Interface Layer

MEA arrays, optical bridges, chemical transducers for bio-digital communication

1000× gain Signal amplification
💻

Digital Processing Layer

Signal processing, hybrid compute, and control systems for orchestration

300-3kHz Bandpass filter
🌡️

Thermal Management Layer

Active cooling, homeostasis control, durability monitoring for substrate health

37.0°C ± 0.5 Target temperature
02

Neural Activity Monitor

Real-time multi-electrode array recording from neuronal cultures

64-Channel MEA Recording
Spike Event
Baseline
03

Silicon vs Wetware

Comparing traditional and biological computing approaches

🔌
Silicon Computing
Energy/op 10⁻¹² J
Adaptability None
Self-repair None
Biocompatibility Poor
🧬
Wetware Computing
Energy/op 10⁻¹⁸ J (10⁶× better)
Adaptability Continuous
Self-repair Automatic
Biocompatibility Native
04

Applications

Where biological-digital hybrids excel

❤️

Biomedical Signal Processing

ECG analysis, neural interfaces, real-time adaptive thresholds with 23% improvement

🌿

Environmental Monitoring

Bacterial biosensors for toxins, parts-per-billion sensitivity, self-replicating networks

🤖

Adaptive Robotics

Soft robotics integration, distributed sensing and control, self-healing capability

🧠

Brain-Computer Interfaces

Wetware buffer for seamless neural interfaces, long-term biocompatible signal processing

Explore Wetware Research

Complete theoretical framework, implementation details, and experimental results.

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