Visualization & Interpretability

What problem does it solve?

Brain_ai systems generate rich internal representations (spike trains, SDRs, workspace states, and reasoning traces) that are difficult to interpret; this skill provides visualization and interpretability tooling to help engineers, researchers, and developers understand, diagnose, and communicate model behavior.

Core Features & Use Cases

• Visualize spike rasters, attention heatmaps, workspace dynamics, and reasoning traces to diagnose issues and communicate insights.
• Generate embeddings projections and dashboard reports to monitor training progress and module interactions.
• Use cases include model debugging, feature analysis, and communicating results to stakeholders.

Quick Start

Run the viz demo to generate sample plots from synthetic brain_ai data.