🍄 psilonet: Psilocybin-Inspired Neural Networks

Architectural research bridging computational neuroscience and transformer efficiency.
Developed by Franz Bettag on Apple Silicon using MLX.

Figure 1: Mechanistic Interpretability Analysis. Left: WikiText-2 induces head-selective collapse (specialization) in mid-layers. Right: TinyStories maintains uniform high alignment. This proves the "psychedelic" connections adapt dynamically to data complexity.

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🚀 Executive Summary

This project implements "Psychedelic Attention"—a novel skip-layer mechanism inspired by psilocybin's effect on functional brain connectivity. By enabling non-hierarchical, learnable pathways between distant layers, we achieve significant performance gains without retraining the base model.

Key Achievements:

• +8.8% Performance Boost: On WikiText-2 using a frozen SmolLM2-135M baseline.
• Novel Architecture: Developed the "Multi-Tap Skip Kernel" which autonomously learns to blend multiple skip distances ($d=3,4,5$) based on layer depth.
• Engineering Efficiency: Implemented Manual Gradient Checkpointing in MLX to enable training complex kernels on consumer hardware (M4 Pro).
• Interpretability: Proved mechanistic distinctiveness via CKA (Centered Kernel Alignment) and SVCCA, revealing a "Novelty Window" where the model diverges from its baseline to process complex dependencies.

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🛠️ Technical Implementation

1. The "Psychedelic" Kernel

Standard transformers process information sequentially ($L_1 \to L_2 \to L_3$). My kernel injects a parallel "skip" stream:

# Conceptual implementation
def forward(self, x, layer_idx, buffer):
    baseline_out = self.local_attention(x)
    
    # "Psychedelic" Path: Attend to historical states
    # Multi-tap allows the model to choose its "time scale"
    psychedelic_out = self.multi_tap_kernel(
        query=x, 
        history=[buffer[layer_idx - d] for d in [3, 4, 5]], 
        weights=self.learnable_tap_logits
    )
    
    return baseline_out + self.alpha * psychedelic_out

2. Engineering Challenges & Solutions

Challenge	Solution	Impact
Memory Constraints	Manual Gradient Checkpointing (mlx.nn.utils.checkpoint)	Enabled 3x larger batches/kernels on Metal
Stability	"Therapeutic Window" Analysis ($\alpha \approx 0.65$)	Identified optimal hyperparams via multi-seed sweeps
Adaptability	Learnable Multi-Tap Softmax	Model autonomously specializes connectivity per layer

3. Tech Stack

• Framework: MLX (Apple's array framework for Silicon)
• Model: SmolLM2-135M (HuggingFace)
• Analysis: CKA, SVCCA, PCA
• Hardware: Apple M4 Pro

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🔬 Research Findings

A. The "Therapeutic Window"

I discovered that the biological metaphor holds true computationally.

• $\alpha < 0.5$: Sub-therapeutic. The signal is too weak, leading to instability.
• $\alpha \approx 0.65$: Optimal. Consistent +8.8% gain with minimal variance.
• $\alpha > 0.7$: Over-saturation. Diminishing returns and higher variance.

B. Emergent Specialization (Multi-Tap)

When given access to multiple skip distances ($d=3,4,5$), the model self-organized:

• Mid-Stack Layers: Prioritized Distance 3 (local context) for feature extraction.
• Late Layers: Shifted toward Distance 4/5 (global context) for output smoothing.
• Data Dependency: Factual data (WikiText) required precise, short-range skips; Narrative data (TinyStories) utilized long-range connections for coherence.

C. Mechanistic Interpretability

I used Centered Kernel Alignment (CKA) to peer inside the "black box":

• Safety: Early layers ($L_0 - L_8$) remain 99% aligned with the frozen baseline, preserving basic syntax and safety capabilities.
• Novelty: Layers $L_9 - L_{27}$ diverge significantly, creating a "psychedelic workspace" where the new connections modify the representation.
• Reconvergence: The final layers re-align, ensuring the output remains compatible with the pre-trained language head.

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📂 Repository Structure

.
├── modules/
│   ├── multitap_psychedelic.py   # The core Multi-Tap implementation
│   ├── psychedelic_smollm.py     # Base architecture & checkpointing
│   └── pretrained_psychedelic.py # Frozen-baseline logic
├── experiments/
│   ├── train_multitap.py         # Training script with tap-weight monitoring
│   ├── alpha_ablation.py         # Hyperparameter sweep logic
│   └── cka_multitap.py           # Mechanistic analysis tools
├── logs/                         # Detailed experiment logs & plots
└── checkpoints/                  # Trained model weights

🚀 Quick Start

1. Setup Environment:

./setup.sh
source .venv/bin/activate

2. Train a Multi-Tap Model:

python experiments/train_multitap.py \
    --distances 3 4 5 \
    --dataset wikitext \
    --train-samples 1000 \
    --epochs 2

3. Run CKA Analysis:

python experiments/cka_multitap.py \
    --dataset wikitext \
    --weights checkpoints/multitap_wiki.npz

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Acknowledgments

This project was accelerated by next-generation AI coding assistants. Special thanks to Claude Code (powered by Claude Opus 4.5), OpenAI Codex 5.0, and Google Gemini 3 Pro for their assistance with mechanistic interpretability writeups, documentation, and resolving complex Python implementation snafus.

Cite

If you find this research helpful, please cite as:

@misc{psilonet,

  author = {Franz Bettag},

  title = {psilonet: Psilocybin-Inspired Neural Architectures and the Multi-Tap Skip Kernel},

  year = {2025},

  publisher = {GitHub},

  journal = {GitHub repository},

  url = {https://github.com/fbettag/psilonet}

}

License

MIT