neuron-model-reconstruction

Reconstruct conductance-based neuron models from spike times using deep learning and DIC.

2|Updated Feb 12, 2026

Search Tags:#deep-learning #neuron-modeling #spike-times #conductance-based #degeneracy #dynamic-input-conductance

Ready to run

npx skills add https://github.com/hiyenwong/ai_collection --skill neuron-model-reconstruction

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: neuron-model-reconstruction
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/neuron-model-reconstruction
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill neuron-model-reconstruction

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

从锋电位时间序列快速重构电导神经元模型，解决神经元参数不可解耦及多解性的问题。

Core Features & Use Cases

基于深度学习与动态输入电导（DIC）框架，提供从尖峰序列到模型参数种群的端到端重构流程。
适用于实验数据分析、神经元参数推断、药物筛选和脑机接口研究等场景。
支持生成具有生物物理可解释性的简并参数集，提升鲁棒性与复现性。

Quick Start

Provide a spike-time sequence to initiate the conductance-based neuron model reconstruction pipeline and obtain an initial population of neuron models.

Frequently Asked Questions about neuron-model-reconstruction

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

FAQPage Schema

How do I reconstruct conductance-based neuron models from spike times?▼

To reconstruct conductance-based neuron models from spike times, provide your spike-time sequence to initiate the deep learning pipeline and obtain an initial population of neuron models.

What is model degeneracy in neuron modeling and how is it handled?▼

Model degeneracy in neuron modeling occurs when multiple parameter sets produce similar spike patterns, handled here by generating biophysically interpretable degenerate parameter sets to improve robustness.

Can I use deep learning to infer neuron parameters from electrophysiology data?▼

You can use deep learning to infer neuron parameters from electrophysiology data by applying dynamic input conductance principles to map spike times to model parameters.

What is the best way to deal with multi-solution problems in neuron parameter inference?▼

The best way to handle multi-solution problems in neuron parameter inference is using iterative compensation workflows to generate robust populations of neuron models with similar spike patterns.

Do I need specific spike-time data formats to start neural model reconstruction?▼

You need to provide a spike-time sequence to initiate the neural model reconstruction pipeline and obtain an initial population of conductance-based neuron models.

Does this neuron modeling approach support brain-computer interface research?▼

This neuron modeling approach supports brain-computer interface research by reconstructing conductance-based models from spike times to analyze electrophysiology data and model degeneracy.