Full Brain Node UI Features Implementation Summary

This document provides a quick reference of implemented Full Brain UI features and where to change them in code. It is intended for frontend implementers and QA engineers.

Overview - Summarizes implemented features for a 24-node Full Brain configuration with layered architecture: PFC, sensing, encoder, inference, decision, memory, learning, aggregator, management layers.

HPDBN alignment (2026-08-26): An implemented UI display represents logical-node configuration and model placement. Local PFC deadline decisions, the Safety Barrier, sensor-fault degradation, and device-specific worst-case execution time still require separate validation.

Implemented features (high level) - PFC dedicated parameter settings (Multi-Modal LM page) - frontend/pages/multi_modal_lm.py — PFC Mode toggle and automatic parameter population (d_model=256, n_heads=8, num_blocks=4) - Motor system TextLM parameter UI (Motor Cortex page) - frontend/pages/motor_cortex.py — Advanced settings card with TextLM params (vocab_size=1024, d_model=64, n_heads=2, num_blocks=2) - Architecture parameter visualization (Spiking LM page) - frontend/pages/spiking_lm.py — model architecture inputs and defaults

Testing & validation - tests/validate_full_brain_ui.py — 27 automated checks (all passed) - Code review and security scans completed

Files modified (summary) - frontend/pages/multi_modal_lm.py — added PFC mode UI and callbacks - frontend/pages/spiking_lm.py — added model architecture UI - frontend/pages/motor_cortex.py — added advanced settings - tests/validate_full_brain_ui.py, tests/test_full_brain_ui.py — new validation/unit tests

Usage examples, parameter reference, and detailed per-page defaults are included in the original document and can be copied into deployment guides or training run commands.

If you want me to produce a compact README with step‑by‑step UI integration for the RAG page, I can generate it next.