BIM 2: Biologically Inspired Model

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BIM 2 is a lightweight, local-first, biologically inspired sequence learning network and interactive 3D neural visualizer. It runs entirely in the browser using vanilla HTML, CSS, and JavaScript.

The model leverages concepts from Hierarchical Temporal Memory (HTM), Sparse Distributed Representations (SDR), and Hebbian learning ("cells that fire together, wire together") to learn sequential relationships between words in real-time and predict future sequences with zero server-side dependencies.

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🌟 Key Features

• 3D Neural Visualizer: A real-time rendering of $16,384$ neurons distributed across a Fibonacci sphere. Active, previous, and predicted neural states are represented with color-coded pulsing glows.
• Interactive Sandbox & Parameter Sliders: Real-time controls to adjust Hebbian Learning Rate, Connection Threshold, and Active Synaptic Decay, allowing users to test network plasticity dynamics instantly.
• Mouse Hover Node & Synapse Tracing: Hovering over any node on the rotating 3D canvas lights it up in glowing yellow, shows its node ID, and traces all its outgoing connected synapses in real-time.
• Active Synaptic Decay (Forgetting): Pragmatically prunes unreinforced pathways over time depending on the decay setting, simulating biological forgetting and preventing network saturation.
• Hebbian Plasticity Engine: Dynamically wires synapses in real-time as you type. Watch the learning state progress from READY to WIRING... and finally to STABLE as sequences are repeated.
• Predictive Sequencing: Recognizes and completes learned sequences (e.g., query "apple is?" to predict "sweet").
• Multi-Step Look-Ahead: Traverses grammatical stop-words (e.g., is, of, the) to predict the next meaningful content word.
• Deterministic SDRs: Maps text input into high-dimensional sparse representations ($16,384$ dimensions, $64$ active bits, $\approx 0.39%$ sparsity) using a deterministic hashing algorithm.
• Surprise Metric: Outputs a real-time surprise score ($0.0$ to $1.0$) based on SDR overlap between prediction and actual input.

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🚀 Quick Start

First, clone the repository and navigate into the project directory:

# Clone the repository
git clone https://github.com/codebyshubham007/ai-bim.git

# Navigate into the project folder
cd ai-bim

Option 1: Double-Click

1. Open the project folder in your file manager.
2. Double-click index.html to open the application directly in any modern web browser.

Option 2: Local Server (Recommended)

To run the project via a local development server (e.g., using Python, Node.js, or VS Code Live Server):

Using Python 3:

python -m http.server 8000

Then visit http://localhost:8000 in your web browser.

Using Node.js (http-server):

npx http-server -p 8000

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🧠 Interactive Training Guide

You can teach the network relationships in real-time:

1. Input a Sequence: Type a simple sequence such as "apple is sweet" and press Send (or press Enter).
2. Observe the Visualization:
   • Active nodes representing the current word pulse in Cyan.
   • Previous nodes fade to Purple.
   • Synaptic lines show orange pathways during the learning phase.
3. Repeat to Wire (3x): Enter the exact same sequence "apple is sweet" three times. You will see:
   • Hebbian Plasticity state change: READY $\rightarrow$ WIRING... $\rightarrow$ STABLE.
   • The number of Concepts Formed increases to 1.
4. Query the Model:
   • Type "apple is" (or "apple is?") and hit Enter.
   • The model will predict "sweet" with $0.00$ surprise.
   • Predicted nodes and stable synaptic paths will glow in Green.

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🛠️ Tech Stack & File Structure

├── index.html       # Mac/Linux terminal-inspired interface & canvas overlay
├── styles.css       # Glassmorphism design system & visual styling
├── app.js           # Deterministic SDR hashing, Hebbian engine, & 3D Fibonacci renderer
└── docs/
    ├── ARCHITECTURE.md          # Technical overview, algorithms, and data structures
    └── LEGAL_AND_COMPLIANCE.md  # Privacy, local-first disclaimer, compliance checklist

• Frontend Structure: Simple single-page app (SPA) layout.
• Styling: Vanilla CSS using custom properties (variables), backdrop filters for glassmorphism, and hardware-accelerated animations.
• Fonts: Outfit (sans-serif) for high-readability UI and JetBrains Mono for stats/data.

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⚙️ Mathematical Parameters

• SDR Dimensions ($N$): $16,384$
• Active Bits ($K$): $64$
• Sparsity: $\approx 0.39%$
• Hebbian Learning Rate ($\eta$): $0.34$
• Synapse Decay Rate ($\delta$): $0.02$
• Connection Threshold ($\theta$): $0.50$
• Overlap Decoding Threshold: $\ge 15$ bits (out of $64$)

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📄 License

This project is licensed under the MIT License. Feel free to use, modify, and distribute it.