MMN Large Scale Simulation

This project implements a large-scale Mismatch Negativity (MMN) simulation using Brian2. It models cortical columns and memory traces to investigate deviance detection mechanisms.

Project Structure

The project has been modularized for better maintainability and readability:

• main.py: The entry point of the simulation. Used to configure parameters and launch experiments.
• src/: Source code directory.
  • network.py: Contains functions to build neuron groups, synapses, and cortical columns.
  • simulation.py: Core logic for running simulations, including paradigm generation (Classic, Alternating, etc.).
  • analysis.py: Functions for analyzing spike data, detecting omission responses, and calculating statistics.
  • plotting.py: Visualization tools for generating raster plots, PSTHs, and weight profile figures.

Installation

Ensure you have Python installed. Install the required dependencies:

pip install -r requirements.txt

Note: This project requires brian2, numpy, and matplotlib.

Usage

To run the simulation:

python main.py

Configuration

You can select the experiment type in main.py by changing the experiment_to_run variable:

• 'classic'
• 'alternating'
• 'local_global'
• 'omission'
• 'figure4_multi' (Reproduces Figure 4 from the reference paper)

Output figures are saved in the fig_out/ directory.