A state-of-the-art interactive simulator for Organoid Intelligence (OI) biocomputing — built to be both scientifically rigorous and accessible to anyone.
Grew brain cells on a chip. Taught them to play Pong. Built a dashboard for it.
bio-synaptic is an interactive web dashboard simulating what happens inside a real organoid intelligence biocomputer lab — where living human brain cells grown on silicon chips are used as biological processors.
Every number in this simulator is grounded in real neuroscience:
- The voltage traces use real Hodgkin-Huxley and Izhikevich neuron models
- Burst detection uses the MaxInterval algorithm (same as MEA-NAP / meaRtools)
- The Pong training loop replicates the actual DishBrain Free Energy Principle feedback
- The ethics panel follows the Baltimore Declaration on Organoid Intelligence
- Benchmark figures come from published Nature and Frontiers papers
🆕 New to this topic? Read
EXPLAINER.md— a plain-English guide from zero to expert, written for fresh grads and curious people.
- L-system axon branching pathfinding on an HTML5 Canvas
- Nerve Growth Factor (NGF) injection triggers growth surges
- Live metrics: cell count, synaptic density, myelination factor
- 64 interactive micro-electrode channels — click to stimulate
- Real-time burst detection (MaxInterval): burst frequency, mean IBI, network synchrony
- Rolling oscilloscope trace
- Spike event counter (4-second rolling window)
| Model | Equations | Accuracy | Cost |
|---|---|---|---|
| Izhikevich | 2 ODEs | Biologically plausible spike shapes | Fast (200 steps/tick) |
| Hodgkin-Huxley | 4 ODEs (Na⁺/K⁺/leak channels) | Nobel Prize-level accuracy (1963) | Full (1000 steps/tick) |
Both models run on all 64 electrodes simultaneously using Euler integration inside the simulation loop.
- DishBrain Pong — biological feedback loop replicating the Cortical Labs experiment
- Logic Gates (AND/OR/XOR) — synaptic logic learning
- Learning efficiency modulated by dopamine, GABA, and synaptic density
- Temperature, Glucose, O₂, Dopamine, GABA sliders
- Cell death triggered by starvation
- Chemical depletion from cell metabolism
- Seizure detection when network synchrony > 70%
- IIT-Φ proxy gauge — simplified Integrated Information Theory measurement
- Sentience risk score (0–100) — calibrated from synchrony × cell count × synaptic density
- Baltimore Declaration compliance checklist — 5 items, live-updating
- Welfare event log — auto-logs threshold crossings
- Welfare levels: Safe → Monitor → Review Required → Halt Protocol
- Power draw comparison (organoid ~10µW vs H100 cluster ~700W)
- Synaptic density (3D biological vs 2D silicon)
- Carbon footprint
- Market projections
- 11-section interactive accordion explaining the science
- Covers: organoids, MEA, DishBrain, AI vs biology, transformers vs neurons, backprop vs Hebbian learning, ethics, and where this is all going
- Written for a 15-year-old with no background
| Layer | Technology |
|---|---|
| Framework | React 18 + TypeScript |
| Build | Vite 6 |
| Styling | Vanilla CSS (glassmorphism, custom animations) |
| Fonts | Google Fonts — Outfit + JetBrains Mono |
| Icons | Lucide React |
| Physics | Custom Hodgkin-Huxley + Izhikevich Euler integration |
| Deploy | Vercel |
No Tailwind. No external charting libraries. All visualisations are raw HTML5 Canvas or CSS.
git clone https://github.com/CodeWreckPro/bio_synaptic
cd bio-synaptic
npm install
npm run devsrc/
├── hooks/
│ └── useSynapticSim.ts # Core simulation engine
│ # — HH + Izhikevich models
│ # — MaxInterval burst detection
│ # — Ethics metrics (IIT-Φ proxy)
│ # — Raster plot spike events
├── components/
│ ├── GrowRoom.tsx # Axon branching canvas
│ ├── ElectrophysiologyGrid.tsx # MEA + oscilloscope + burst metrics
│ ├── TrainingPlayground.tsx # Pong + logic gates
│ ├── IncubatorControls.tsx # Life support dashboard
│ ├── Benchmarks.tsx # Silicon vs synaptic comparisons
│ ├── EthicsPanel.tsx # Baltimore Declaration compliance widget
│ └── About.tsx # Plain-English explainer accordion
├── App.tsx # Lab shell — model toggle, sidebar, routing
├── index.css # Bio-cybernetic design system
└── main.tsx
EXPLAINER.md # Deep-dive guide for fresh grads
| Feature | Real-World Reference |
|---|---|
| DishBrain Pong training | Kagan et al., Nature Electronics (2022) |
| Baltimore Declaration ethics | Bhanu et al., Nature (2024) |
| FinalSpark neuroplatform | finalspark.com, Frontiers in Neuroscience (2024) |
| Hodgkin-Huxley model | Hodgkin & Huxley, Journal of Physiology (1952) — Nobel Prize 1963 |
| Izhikevich model | Izhikevich, IEEE Trans Neural Networks (2003) |
| MaxInterval burst detection | Pasquale et al., Journal of Neuroscience Methods (2010) |
| IIT / Φ | Tononi et al., BMC Neuroscience (2008) |
| Energy efficiency figures | FinalSpark whitepaper (2024), Cortical Labs CL-1 datasheet |
Conceived, researched, and built by @CodeWreckPro — with the assistance of Antigravity, an agentic AI coding assistant by the Google DeepMind Advanced Agentic Coding team.
MIT — do whatever you want with it. If you publish research using this simulator, a citation would be appreciated.