Update README

README.md

@@ -1,9 +1,11 @@
 # LearnPI
 
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-Instructions to reproduce all main text, supplementary and mathematical appendix figures of the manuscript
-"Learning accurate path integration in ring attractor models of the head direction system".
+Instructions to reproduce all main text, supplementary and appendix figures of the manuscript
+"Learning accurate path integration in ring attractor models of the head direction system". The code to 
+reproduce results can be found in https://github.com/panvaf/LearnPI. Place a folder containing the code in
+the parent folder downloaded from this data repository.
 
 Instructions are based on default values of various parameters. After generating figures, please remember 
 to revert parameters to these default values.
@@ -12,43 +14,36 @@ The figures are reproduced from saved networks. In order to generate the results
 run_simulation.py with the desired parameters for each figure mentioned below. Simulation takes ~8 hours
 on a mid-range gaming laptop, while the reduced network drastically reduces simulation time to ~3 minutes.
 
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-- Fig. 1B,C, 2A-C, 3A-C,E,F, S3: Run generate_plots.py with vel_hist = True.
+- Fig. 1B,C, 2A-C, 3A-C,E,F, Fig. 3 - Fig. Sup. 2: Run generate_plots.py with vel_hist = True.
 
-- Fig. 2D: Run stability.py.
+- Fig. 1 - Fig. Sup. 1: Run EB_synapses.py. The upper-left panel in A also appears in Fig. 1E.
 
-- Fig. 3D, 4, S5E, S6A-C, S7A: Run multinet_plots.py. The diffusion coefficients for Fig. S5E were obtai-
- ned by running stability.py with d_coeff = True for the corresponding networks.
+- Fig. 2D: Run stability.py.
 
-- Fig. S1: Run EB_synapses.py. The upper-left panel in Fig. S1A also appears in Fig. 1E.
+- Fig. 3D, 4, Fig. 4 - Fig. Sup. 1A, App. 1 - Fig. 1E, App. 2 - Fig. 1A-C: Run multinet_plots.py. The diffusion coefficients for Fig. S5E were obtained by running stability.py with d_coeff = True for the corresponding networks.
 
-- Fig. S2: Run generate_plots.py with cut_exc = True.
+- Fig. 3 - Fig. Sup. 1: Run generate_plots.py with cut_exc = True.
 
-- Fig. S4: Run generate_plots.py with data_dir = '\\savefiles\\trained_networks\\Parallel\\Perturb_Conn\\'.
- The networks were randomly generated, therefore results can vary. By varying the parameter 'run' in the 
- dictionary params from 0 to 11, one can see multiple examples of such networks.
+- Fig. 3 - Fig. Sup. 3: Run generate_plots.py with data_dir = '\\savefiles\\trained_networks\\Parallel\\Perturb_Conn\\'. The networks were randomly generated, therefore results can vary. By varying the parameter 'run' in the dictionary params from 0 to 11, one can see multiple examples of such networks.
 
-- Fig. S5A-D: Run generate_plots.py with 'n_sigma': 0.7 in the dictionary params.
+- Fig. 4 - Fig. Sup. 1B: Run generate_plots.py with PI_err = False, and 'gain': .125 in the dictionary params.
 
-- Fig. S6D,E: Run generate_plots.py with sim_run = '4Medium', PI_err = False and 'tau_s': 1 in the dictio-
- nary params.
+- Fig. 4 - Fig. Sup. 1C: Run generate_plots.py with PI_err = False and 'gain': 10 in the dictionary params. Also plot margins in lines 761 and 766 need to be adjusted.
 
-- Fig. S7B: Run generate_plots.py with PI_err = False, and 'gain': .125 in the dictionary params.
+- Fig. 4 - Fig. Sup. 1D-F: Run generate_plots.py with PI_err = False, sim_run = 'Long', and 'gain': -1 in the dictionary params.
 
-- Fig. S7C: Run generate_plots.py with PI_err = False and 'gain': 10 in the dictionary params. Also plot
- margins in lines 761 and 766 need to be adjusted.
+- App. 1 - Fig. 1A-D: Run generate_plots.py with 'n_sigma': 0.7 in the dictionary params.
 
-- Fig. S7D-F: Run generate_plots.py with PI_err = False, sim_run = 'Long', and 'gain': -1 in the dictiona-
- ry params.
+- App. 2 - Fig. 1D,E: Run generate_plots.py with sim_run = '4Medium', PI_err = False and 'tau_s': 1 in the dictionary params.
 
-- Fig. S8: Run generate_plots.py with 'vary_w_rot': True and 'adj': True in the dictionary params.
+- App. 3 - Fig. 1: Run generate_plots.py with 'vary_w_rot': True and 'adj': True in the dictionary params.
 
-- Fig. S9: Run generate_plots.py with PI_example_dir = '\\savefiles\\PI_example1.npz', 'vary_w_rot': True
- in the dictionary params and filename = "fly_rec" + util.filename(params) + 'NoLearn'. Also set 
+- App. 3 - Fig. 2: Run generate_plots.py with PI_example_dir = '\\savefiles\\PI_example1.npz', 'vary_w_rot': True in the dictionary params and filename = "fly_rec" + util.filename(params) + 'NoLearn'. Also set 
  err_lim = 180 in line 487.
 
-- Fig. S10: Run generate_plots.py with PI_example_dir = '\\savefiles\\PI_example_360_max.npz', PI_err = 
+- App. 3 - Fig. 3: Run generate_plots.py with PI_example_dir = '\\savefiles\\PI_example_360_max.npz', PI_err = 
  False, and 'rand_w_rot': True in the dictionary params.
 
-- Fig. A1-4: Run math_appendix.py.
+- App. 5 - Fig. 1-4: Run math_appendix.py.