Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install hiyenwong-ai-collection-collection-skills-dendrocentric-snn-event-classificationgit clone https://github.com/hiyenwong/ai_collection.gitcp ai_collection/SKILL.MD ~/.claude/skills/hiyenwong-ai-collection-collection-skills-dendrocentric-snn-event-classification/SKILL.md--- name: dendrocentric-snn-event-classification description: "DendroNN dendrocentric neural network methodology for energy-efficient classification of event-based data. Incorporates dendritic computation principles into SNNs for improved spatiotemporal processing. Applies to: event-based vision, neuromorphic computing, dendritic computation, energy-efficient classification. Activation: dendrocentric neural, DendroNN, dendritic computation, event-based classification, dendrite-inspired SNN." --- # DendroNN: Dendrocentric Neural Networks > Energy-efficient neural networks incorporating dendritic computation principles for classification of event-based (neuromorphic) data. ## Metadata - **Source**: arXiv:2603.09274 - **Published**: 2026-03-XX - **Category**: cs.LG ## Core Methodology ### Key Innovation Integrates **dendritic computation** — the non-linear processing capabilities of biological neuron dendrites — into artificial neural network architectures for efficient processing of event-based data streams. ### Biological Inspiration - Dendrites perform non-linear spatial and temporal integration of synaptic inputs - Dendritic branches act as semi-independent computational subunits - Local dendritic spikes enable hierarchical feature extraction - Branch-specific plasticity supports efficient learning ### Technical Framework 1. **Dendritic Compartment Model**: Each neuron has multiple dendritic branches with independent processing 2. **Event-Based Processing**: Designed for neuromorphic sensor data (event cameras, DVS) 3. **Energy Efficiency**: Sparse event-driven activation with dendritic computation 4. **Spatiotemporal Integration**: Both spatial (branch-level) and temporal (spike-timing) processing ## Applications - Event-based vision classification - Low-power edge AI with neuromorphic sensors - Real-time object recognition with event cameras - Energy-constrained autonomous systems ## Pitfalls - Dendritic compartment models increase parameter count - Training requires specialized dendritic learning rules - Event-based data preprocessing can be complex - Hardware support for dendritic computation is limited ## Related Skills - dual-memory-pathway-snn - snn-internal-noise-analysis - edgespike-edge-iot-snn