ruvector-spiking-neural

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.