Dynamic Engrams Model

ISTA Internship at Prof. Tim Vogels group

Project title: Cross-Implementation and Verification in Brian2 of the Dynamic Engrams Consolidation Model

Internship dates: June - September 2025

This project is a verification of a memory consolidation simulation with a Spiking Neural Networks (SNN) model implemented in Brian2 (Python), originally defined in Auryn (C++) in:

• Tomé, D.F., Zhang, Y., Aida, T. et al. (2024)
  Dynamic and selective engrams emerge with memory consolidation.
  Nature Neuroscience 27, 561–572
  https://doi.org/10.1038/s41593-023-01551-w

Project structure

/analysis/           # Functions for analyzing simulation outputs
/config/             # Global parameters and save utilities
/model/              # Core neuron and synapse models
/network/            # Network construction and connectivity functions
/scripts/            # Main execution scripts
    generate_stim_input_map.py
    monitor_stable_activity.py
    run_many.sh
    simulate_and_probe.py
/src/                # Simulations' outputs
/stimuli/            # Stored external input patterns
    squarev5_cues_1-4.pat
    squarev5.pat
requirements.txt
1_activity_plots.ipynb
2_probing_n_recall_plots.ipynb 

Setup

1. Create and activate a virtual environment

python -m venv venv
source venv/bin/activate

2. Install dependencies

pip install -r requirements.txt

3. Generate the receptive field (stimulus → input map)

python -m scripts.generate_stim_input_map

4. Run the pipeline

python -m scripts.simulate_and_probe

Note

To run only the burn-in phase with monitors recording the activity of both excitatory and inhibitory populations:

python -m scripts.monitor_stable_activity

Analysis

The notebook 2_probing_n_recall_plots.ipynb loads and visualizes the data saved by the main simulation. Whereas, the notebook 1_activity_plots.ipynb loads the data generated by the only burn-in simulation.

Notes

All scripts should be executed from the project root so that relative imports work correctly.