Codes for simulations and analyses in Ghandour, Haga, Ohkawa et al., "Past memories are conserved via reactivation whereas future memories are prepared via offline synaptic plasticity mechanisms during idling moments".
Codes depend on Python 3, numpy, scipy, matplotlib, and a UNIX shell script (bash). With Anaconda (or Miniconda), you can run conda env create -f env_engramtobe.yml then conda activate engramtobe to create an appropreate environment.
For running the codes, execute bash batch.sh on a UNIX shell (we ran codes in Ubuntu 20.04 and 22.04). Otherwise, manually execute commands written in the batch.sh.
Codes execute 5 simulations for each of four conditions (with and without sleep, homeostatic plasticity OFF, LTD-OFF). Each simulation generates a .npz file that contains results. After 5 simulations, results are summarized to output .csv and .tiff files that show cell-type ratios, coincidence and correlation of neural activities, and matching ratios as presented in the paper.
It took approximately 10 minutes to finish all calculations by a workstation with Xeon-W7-3445.