ruvector-spiking-neural

Simulate Spiking Neural Networks with SIMD optimization and STDP learning.

1|Updated Feb 8, 2026

Search Tags:#machine learning #deep learning #spiking neural network #snn #neuromorphic computing #ai simulation

Ready to run

npx skills add https://github.com/ricable/cli-skills-builder --skill ruvector-spiking-neural

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ruvector-spiking-neural
Source: https://github.com/ricable/cli-skills-builder/tree/main/.claude/skills/ruvector-spiking-neural
Command: npx skills add https://github.com/ricable/cli-skills-builder --skill ruvector-spiking-neural

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a high-performance engine for building and simulating Spiking Neural Networks (SNNs), enabling the creation of advanced neuromorphic computing applications and energy-efficient AI.

Core Features & Use Cases

High-Performance Simulation: Utilizes SIMD optimization for fast SNN simulations.
Flexible Neuron Models: Supports various neuron models like Izhikevich, LIF, and Hodgkin-Huxley.
Learning Rules: Implements Spike-Timing Dependent Plasticity (STDP) for dynamic network adaptation.
Use Case: Develop energy-efficient AI for edge devices or simulate complex biological neural systems for research.

Quick Start

Install the library and create a new SpikingNetwork with a Poisson input layer, an Izhikevich hidden layer, and a LIF output layer, then simulate it for 1000 timesteps.

Frequently Asked Questions about ruvector-spiking-neural

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a spiking neural network for neuromorphic computing?▼

Build a spiking neural network by creating a SpikingNetwork with Poisson input, Izhikevich hidden, and LIF output layers to simulate brain-like networks for neuromorphic computing.

What is STDP learning in a spiking neural network simulation?▼

STDP learning is Spike-Timing Dependent Plasticity, a rule implemented to enable dynamic network adaptation in spiking neural networks based on the timing of neuron spikes.

Can I simulate Hodgkin-Huxley and LIF neuron models in one network?▼

Yes, you can simulate flexible neuron models including Hodgkin-Huxley, LIF, and Izhikevich simultaneously within a single SpikingNetwork configuration for biological research.

How do I run energy-efficient AI inference with spiking neurons?▼

Run energy-efficient AI inference by configuring spiking neurons with SIMD optimization, enabling fast simulations suitable for edge devices and low-power applications.

Does this spiking neural network engine support SIMD optimization for fast simulations?▼

Yes, the SNN engine utilizes SIMD optimization to deliver high-performance spiking neural network simulations necessary for complex neuromorphic computing applications.

When should I use spiking neural networks instead of traditional deep learning?▼

Use spiking neural networks when developing energy-efficient AI for edge devices or simulating complex biological neural systems, tasks where traditional deep learning is less efficient.

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