Code and data for the paper The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning

Experimental data

Variables in trial_data.csv :

• sub: subject ID
• phase: task stage. 0 for initial learning, 1 for initial learning
• condition: experimental group. 'OFF' - light-off control, 'ON' - light-on ACh-suppressed group, 'GFP' - light-on control
• trial: recoded trial variable. 1.1 for trial 1 on day 1, 1.2 for trial 2 on day 1 ...
• rew.found: 1 if found, 0 if not
• day: 1 - 8 for initial learning, 1 - 12 for reversal learning

Processed and summarized data can be accessed in processed_trial_data.rds using R

> datalist <- readRDS("../../data/experiments/processed_trial_data.rds")
> names(datalist)
[1] "combined.dat"    "mouse.success.p" "group.success.p" "ids"            
[5] "hit.80.ids"      "hit.80"

• combined.dat: raw data grouped by subject ID in a tibble
• mouse.success.p: successful trials per day of each mouse
• group.success.p: group average performance
• hit.80.ids: number of days taken to reach 80% performance with subject IDs

Code

sn-Plast model

• run_model.py: Python implementation of the sn-Plast model (Brzosko et al., 2017) adapted to the experimental open-field task. To display plots as the experiment is being run do python run_model.py -p -it 1. If not plotting, model simulation results are saved to .csv files.
• process_model_results.R: R script to read in and process model simulation results for analysis
• param_search.sh: example bash script to run the model across different parameter settings (e.g. )

Model fitting and analysis

• analyze_experimental.R : statistical tests of experimental behavioural data
• analyze_simulations.R : model-fitting to experimental behavioural data