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zihao-cpu/Homo_DMF

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🧠 Homeostatic Dynamic Mean Field Model (DMF_ISP)

This repository contains Python code for simulating a homeostatic dynamic mean field (DMF) model with synaptic plasticity and generating synthetic BOLD signals. The model implements biologically inspired excitatory-inhibitory dynamics using a structural connectivity (SC) matrix derived from human brain data. It supports noise, homeostatic inhibitory plasticity, and simulation of functional connectivity (FC).


πŸ“‚ Repository Contents

DMF_ISP/
β”œβ”€β”€ BOLDModel.py # Module for simulating BOLD signals from firing rates
β”œβ”€β”€ DMF_ISP_numba.py # Core DMF model with inhibitory synaptic plasticity (Numba-accelerated)
β”œβ”€β”€ run_DMF_ISP.py # Example script to run DMF simulation and generate FC matrix
β”œβ”€β”€ SCmatrices88healthy.mat # Structural connectivity matrix averaged across 88 healthy participants

🧬 Scientific References

Structural Connectivity Source

Škoch, A., RehÑk BučkovÑ, B., Mareő, J., et al. (2022).
Human brain structural connectivity matrices–ready for modelling.
Scientific Data, 9, 486. https://doi.org/10.1038/s41597-022-01596-9

DMF Model (Preprint)

Mindlin, I., Coronel-Oliveros, C., Sitt, J. D., CofrΓ©, R., Luppi, A., Andrillon, T., & Herzog, R. (in preparation).
A homeostatic dynamic mean field model: enhanced stability and state repertoire.


βš™οΈ Installation

  1. Clone the repository:
git clone https://github.com/your-username/DMF_ISP.git
cd DMF_ISP
  1. Install the required Python dependencies:
pip install numpy scipy matplotlib numba

πŸš€ Usage

To run a basic simulation:

python run_DMF_ISP.py

This will:

  • Load the structural connectivity matrix
  • Run the DMF model with or without plasticity
  • Generate synthetic BOLD signals
  • Compute and display a functional connectivity matrix

🧠 Model Description

  • Nodes: 90 brain regions
  • Inputs: Structural connectivity (SC), noise
  • Outputs: Firing rates, BOLD signals, FC matrix
  • Plasticity: Homeostatic inhibitory plasticity targeting a fixed mean firing rate
  • Numerical Integration: Euler-Maruyama with Gaussian noise
  • Optimization: Numba-accelerated for performance

πŸ“Š Outputs

  • BOLD signals: simulated low-frequency hemodynamic response
  • FC matrix: pairwise Pearson correlations between regions
  • Plots: connectivity matrix visualization

πŸ“¦ Dependencies

  • numpy
  • scipy
  • matplotlib
  • numba

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Homeostatic Dynamic Mean Field Model (DMF_ISP)

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