phase-brain-optimize

Name: phase-brain-optimize
Availability: InStock
Author: stanislavjiricek

Guide parameter exploration and model fitting for computational neuroscience.

6|Updated Mar 8, 2026

Search Tags:#workflow #optimization #model fitting #computational neuroscience #parameter sweep

Ready to run

npx skills add https://github.com/stanislavjiricek/neuroflow --skill phase-brain-optimize

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: phase-brain-optimize
Source: https://github.com/stanislavjiricek/neuroflow/tree/main/skills/phase-brain-optimize
Command: npx skills add https://github.com/stanislavjiricek/neuroflow --skill phase-brain-optimize

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the process of parameter exploration and model fitting, enabling users to efficiently map model behavior across a parameter space or optimize parameters to precisely match experimental data.

Core Features & Use Cases

Parameter Sweeping: Explore how model outputs change with variations in input parameters.
Model Fitting: Optimize model parameters to best replicate observed experimental results.
Use Case: A neuroscientist wants to find the optimal set of parameters for a computational model of a neural circuit that best explains experimental recordings. This Skill guides the process of defining the search space, selecting an optimization algorithm, and running the fitting procedure.

Quick Start

Use the /neuroflow:brain-optimize command to begin optimizing model parameters.

Frequently Asked Questions about phase-brain-optimize

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

FAQPage Schema

How do I optimize parameters for a computational neuroscience model?▼

Model fitting in computational neuroscience requires defining optimization goals, writing an optimization plan, and executing search algorithms like grid search, differential evolution, or Bayesian optimization to match experimental data.

What is parameter sweeping in computational neuroscience?▼

Parameter sweeping explores how model outputs change with variations in input parameters, enabling researchers to map model behavior across a parameter space efficiently.

What's the best way to fit a neural circuit model to experimental recordings?▼

Fitting a neural circuit model to experimental recordings involves defining the search space, selecting an optimization algorithm, and running the fitting procedure to find the optimal parameter set.

Can I use Bayesian optimization for parameter exploration in computational models?▼

Yes, Bayesian optimization is supported for parameter exploration. The Skill guides executing search algorithms including grid search, differential evolution, and Bayesian optimization to map and optimize model behavior.

Do I need to define optimization goals before fitting a computational model?▼

Yes, defining optimization goals is required before fitting a computational model. This step guides the parameter search process to ensure optimized parameters precisely match observed experimental data.

When should I use grid search versus differential evolution for model fitting?▼

Grid search systematically maps model behavior across a parameter space, while differential evolution optimizes parameters to match experimental data. Choose based on whether you are exploring or precisely fitting the model.