This is the codes to replicate the fitting, prediction, and simulation analysis of the reinforcement model in Medial prefrontal cortex suppresses reward-seeking behavior with risk of punishment by reducing sensitivity to reward, Nishio et al., 2024, Frontiers in Neuroscience doi: 10.3389/fnins.2024.1412509.
The code is initially executed in MATLAB 2023a on a Mac. Install following MATLAB packages
- Optimization Toolbox [https://www.mathworks.com/products/optimization.html]
- Statistics and Machine Learning Toolbox [https://www.mathworks.com/products/statistics.html] Clone this repository.
Following files include behavioral information during training sessions.
- wholeses_airpuff.mat
- wholeses_omission.mat
The information below is included in each file.
| Column | Description |
|---|---|
| success | Lever successfully pulled (0/1) |
| Cue1 | tone A was presented (0/1) |
| Cue2 | tone B was presented (0/1) |
| reward | Reward (water) was presented (0/1) |
| punish | Punishment (airpuff) was presented (0/1) |
| RT | Response Time |
| ses_len | Number of trials for each training session |
| ses_len1 | Number of tone A trials for each training session |
| ses_len2 | Number of tone B trials for each training session |
Following files include behavioral information for acsf/muscimol sessions.
- airpuff_acsf_history.mat
- airpuff_muscimol_history.mat
- omission_acsf_history.mat
- omission_muscimol_history.mat
The information below is included in each file.
| Column | Description |
|---|---|
| trial_idx | ID of each trial |
| success | Lever successfully pulled (0/1) |
| Cue1 | tone A was presented (0/1) |
| Cue2 | tone B was presented (0/1) |
| reward | Reward (water) was presented (0/1) |
| punish | Punishment (airpuff) was presented (0/1) |
| RT | Response Time |
| consumatoryLick | Number of licks after reward presentation |
| anticipatoryLick | Number of licks before go cue |
| earlypull | Number of pulls before go cue |
| analyzed | Trials prior to obtaining 60% of the total planned reward (0/1) |
| LeverPullDuration | Duration of lever pulled above the threshold |
| LickDuration | Duration of licking |
| PullSpeed | Speed of pulling lever |
To reproduce Figure 2,
Fig2_fitting.mrepeats fitting 5000 (50x100) times and plots the fitted parameters. Executing this code takes about 3 days; therefore, we pre-store the parameters in advance in the 'param/' directory.Fig2_fitting_result_aggregation.maggregates 100 fitting results and chooses the parameters of the maximum log likelihood.
To reproduce Figure 4,
Fig4_parameter_plotting.mplots fitted parameters.
To reproduce Figure 6,
Fig6_optimization_plotting_airpuffplots optimization results for the airpuff task.
To reproduce Supplementary Figure 1,
SupFig1_prediction.mplots prediction results.
To reproduce Supplementary Figure 2,
SupFig2_simulation.mrepeats simulation 1000 times and output plotted figures of simulation results and RMSE averaged across 1000 simulation results.
To reproduce Supplementary Figure 3,
SupFig3_optimization.mconducts optimization of each parameter and outputs the optimized parameters.SupFig3_trialsimulation.mplots simulation results using the original parameters.SupFig3_trialsimulation_optimized.mplots simulation results using optimized parameters.
To reproduce Supplementary Figure 5,
SupFig5_optimization_plotting_omissionplots optimization results for the omission task.