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garochee33
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lava-neuro-sim

Run Intel Lava spiking neural network simulations on a CPU backend.

3|Updated Apr 18, 2026
Search Tags:#simulation#python 3.10#spiking neural network#neuromorphic#lava#loihi 2
One-click install
npx skills add https://github.com/garochee33/DSH --skill lava-neuro-sim
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lava-neuro-sim
Source: https://github.com/garochee33/DSH/tree/main/kb/claude/skills/lava-neuro-sim
Command: npx skills add https://github.com/garochee33/DSH --skill lava-neuro-sim

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you run Intel Lava spiking neural network simulations on a CPU backend when physical Loihi 2 hardware is unavailable, while keeping the same source code path for future hardware execution.

Core Features & Use Cases

  • Neuromorphic simulation: Executes LIF and Dense-based spiking networks in Lava with configurable neuron counts and timestep lengths.
  • Public-safe metrics: Returns aggregate outputs such as total spikes, active neurons, sparsity, mean firing rate, and first-spike timing without exposing raw per-neuron data.
  • Hardware-compatible workflow: Supports the same code on Loihi 2 silicon when NxSDK-authorized hardware is available.
  • Use case: Evaluate an E8-structured 240-neuron network, inspect sparsity and firing dynamics, and compare simulation behavior before moving to hardware.

Quick Start

Run the Lava sidecar Python environment to verify the framework is available, then execute the canonical simulation script with your desired neuron count and timestep settings.

Frequently Asked Questions about lava-neuro-sim

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

FAQPage Schema
How do I simulate Intel Lava spiking neural networks when Loihi 2 hardware is unavailable?▼

You can simulate Intel Lava spiking neural networks on a CPU backend using Loihi2SimCfg execution, which keeps the same source code path for future hardware deployment.

What aggregate metrics can I extract from a Lava spiking neural network simulation?▼

Lava spiking neural network simulations return aggregate-only metrics such as total spikes, active neurons, sparsity, mean firing rate, and first-spike timing without exposing raw per-neuron data.

Do I need Python 3.10 to run Lava neuromorphic simulations?▼

Yes, running Lava neuromorphic simulations with this workflow requires Python 3.10, along with Lava nc imports and Loihi2SimCfg execution configuration.

Can I evaluate E8-structured connectivity in a Lava simulation before moving to Loihi 2 silicon?▼

Yes, you can evaluate E8-structured connectivity experiments and inspect sparsity and firing dynamics in a CPU simulation before moving to authorized Loihi 2 NxSDK hardware.

What are the limitations of running Lava simulations on a CPU backend?▼

CPU backend Lava simulations require Loihi2SimCfg execution and only provide aggregate-only metric reporting, meaning raw per-neuron data is not exposed and true hardware execution requires NxSDK authorization.

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