Aurora Synaptic Engine v2 (SpoonOS-Native AI4Science System)

Purpose and Framing

Synaptic Engine v2 repositions the original TreeHacks concept as a reproducible AI4Science research platform. It focuses on mapping the latent geometry of cognitive activity from multi-modal neural and biometric streams, pairing hierarchical manifold learning with agentic experimentation on SpoonOS. The system targets scientific discovery and hypothesis testing rather than unverifiable BCI performance claims.

Core Principles

• Scientific credibility: Demonstrate clustering, pruning, and temporal coherence with synthetic or recorded signals; avoid unvalidated throughput claims.
• AI4Science alignment: Provide workflows for hypothesis generation, manifold analysis, and experiment comparison across tasks or subjects.
• Agentic backbone: Four SpoonOS agents orchestrate acquisition, manifold refinement, decoding, and scientist-facing reporting.
• Buildable in 48–72 hours: Minimal viable encoder, hierarchical clustering stack, and visualization with clear input→output loops.

System Architecture

Raw EEG-like + biometric streams ──► Multi-Modal Encoder ──► z_t latent
                                      (1D conv → transformer → fusion)
         │
         ▼
Hierarchical Engine
- coarse clusters (k≈5–10)
- recursive subclusters (k≈5–10 each)
- posterior-based pruning
- temporal smoothing (moving window / HMM)
         │
         ▼
State posterior ──► DecoderAgent (text/image/task outputs)
         │
         ▼
SpoonOS Agent Layer
1) AcquisitionAgent
2) ManifoldAgent
3) DecoderAgent
4) NeuroscientistAgent
         │
         ▼
Scientist UI: manifold viz, trajectories, pruning curves, temporal coherence

Multi-Modal Encoder (MVP)

• Inputs: synthetic EEG (sinusoid/ERP mixtures), biometric channels (HRV, EDA), optional text or CLIP embeddings.
• Architecture: lightweight 1D conv stack → transformer encoder → mean pooling; attention-based fusion for optional modalities.
• Outputs: latent vector z_t ∈ ℝ^D per window.

Hierarchical Engine

• Level 1 coarse clustering (k≈5–10) on latent batches.
• Level 2+ recursive subclustering per parent cluster (k≈5–10 each) with on-demand refinement.
• Pruning: drop subtrees whose posterior mass falls below threshold; logarithmic reduction of search space (target 7–8× per layer in simulation).
• Temporal coherence: moving-window smoothing or lightweight HMM to stabilize labels across timesteps; report stability gains (e.g., >40% consistency in tests).

SpoonOS Agent Roles

• AcquisitionAgent: streams real or synthetic data; calls /encode to produce latents.
• ManifoldAgent: updates hierarchy, triggers subclustering, executes pruning, tracks temporal metrics.
• DecoderAgent: infers cognitive state posterior, emits text/image/task-level outputs, exposes uncertainty.
• NeuroscientistAgent: designs experiments, compares manifolds across tasks/subjects, surfaces hypotheses and interpretability notes.

Input→Output Demo Loop

1. Raw window from synthetic EEG + biometrics.
2. Encoder produces latent z_t.
3. ManifoldAgent clusters and prunes hierarchy.
4. Temporal smoother refines state; DecoderAgent reports current cognitive hypothesis.
5. UI renders dendrogram, latent trajectory (UMAP/t-SNE), pruning curve, and temporal coherence plot with live updates.

Scientific Workflows

• Hypothesis testing: NeuroscientistAgent suggests contrasts (task A vs B), runs clustering per condition, and reports structural differences.
• Ablations: evaluate impact of pruning thresholds, temporal window sizes, and fusion strategies on stability and search-space reduction.
• Cross-subject comparison: align manifolds via Procrustes/CCA on shared stimuli, reporting cluster correspondence scores.

Evaluation Metrics

• Search-space reduction per layer (target 7–8×).
• Temporal consistency improvement from smoothing (report % gain over raw labels).
• Cluster purity/entropy on synthetic labeled datasets.
• Latent trajectory smoothness (e.g., velocity/curvature stats) to evidence manifold structure.

Neo/Web3 (Optional)

• Log experiment hypotheses and cluster prototypes on-chain for provenance.
• Record agent-to-agent decisions as verifiable events aligned with SpoonOS spec.

Implementation Plan (48–72 Hours)

1. Data + Encoder: generate synthetic EEG/biometric streams; implement 1D conv + transformer encoder with fusion.
2. Hierarchy Core: coarse k-means/HDBSCAN, recursive subclustering, posterior-based pruning, and temporal smoothing.
3. Agent Layer: four SpoonOS-compliant scripts wrapping encode/cluster/infer/report endpoints.
4. Visualization: dendrogram, latent UMAP/t-SNE trajectory, pruning and temporal coherence plots; sidebar for decoded state + uncertainty.
5. Experiments: run ablations on pruning thresholds and smoothing windows; capture metrics and screenshots for presentation.

Deliverables

• Live demo showing streaming data → embeddings → cluster hierarchy → pruned/temporal-stabilized decoding.
• Scientist UI with manifold visualization and interpretability overlays.
• Technical report (PDF) covering architecture, pruning/temporal equations, and ablation results.
• SpoonOS agent configs and scripts for reproducibility.
• GitHub repo documenting workflows and commands for judges.

Running the demo pipeline

The synaptic_engine package now includes executable components that reproduce the agent flow end-to-end with synthetic data.

1) Install dependencies

python -m venv .venv
source .venv/bin/activate
pip install numpy matplotlib scikit-learn

2) Execute the SpoonOS agent loop

python -m src.synaptic_engine.demo --windows 128 --output artifacts

This simulates EEG + biometric streams, encodes latents, builds a recursive hierarchy, prunes low-posterior branches, and applies temporal smoothing. Saved artifacts include:

• latent_trajectory.png: PCA projection of latent dynamics.
• hierarchy.png: pruned dendrogram view.
• temporal_coherence.png: raw vs. smoothed state trajectories.

3) Extend or swap components

• Replace SyntheticConfig in demo.py with real data loaders to test hardware recordings.
• Adjust HierarchyConfig to explore pruning thresholds, branch factors, and temporal windows.
• Integrate additional modalities by extending MultiModalEncoder.encode with new fusion projections.

4) Judge-facing experiments

• Report average branch reduction (~search-space) and temporal stability printed by the demo.
• Generate side-by-side runs with different pruning thresholds to showcase 7–8× reductions per layer.
• Export the plots into your presentation deck to visualize manifold structure and smoothing benefits.