This repository contains the MATLAB post-processing code and data for analyzing stochastic Hodgkin-Huxley neural networks under event-based control strategies. The code implements numerical solutions of Hamilton-Jacobi-Bellman equations and provides tools for analyzing both single neuron and population-level dynamics.
- MATLAB scripts for post-processing HJB solutions and optimal control analysis
- Visualization tools for control signals, phase space trajectories, and system dynamics
- Complete data files including HJB solutions (
phi_*.dat) and optimal control signals (uStar_*.dat) - Analysis pipeline for both single neuron and population studies with various noise levels
- Monte Carlo simulation capabilities for stochastic systems
- MATLAB R2020a or newer
- MATLAB Statistics and Signal Processing Toolbox
- MATLAB Optimization Toolbox
- Clone the repository:
git clone https://github.com/faranakR/HH-Stochastic-Control.git
- Add all subfolders to the MATLAB path: addpath(genpath('code')); addpath(genpath('__Output'));
- Run example analysis: cd code/main_scripts main_HH2D_stochastic
code/main_scripts/: Main analysis scripts for running simulations code/visualization/: Tools for plotting and visualization of results code/functions/: Core analysis functions including: HJB solution processing Stochastic integration Monte Carlo simulations Event-based control implementation __Output/: Complete simulation data for various noise levels Data Description The __Output directory contains:
D_/: Subdirectories for different noise levels phi_.dat: Cost-to-go function solutions uStar_*.dat: Optimal control signals timeMat.txt, timeMat2.txt: Time evolution data
If you use this code in your research, please cite:
[Citation information will be added upon publication]
MIT License. See the LICENSE file for details.
For questions about the code, please open an issue on GitHub.
Release Information
Version: v1.0 Author: @faranakR