Investigate A plus A minus combination

.gitignore

@@ -130,3 +130,6 @@ data/
 cerebellum_NODS.hdf5
 cerebellum_NODS_smaller.hdf5
 p.p
+
+#PLOT
+.html

compare_w-wo_NO/grc_sdf_raster.py

@@ -1,3 +1,4 @@
+
 import sys
 import os
 
@@ -24,18 +25,18 @@
 without_NO_color = "#000000"
 cell = "granule_spikes"
 
-results_path = f"/media/amtra/Samsung_T5/EBCC_4Hz/"
+results_path = f"/home/nomodel/code/NODS/results/grid_search/grid_NO/test/"
 spk = get_spike_activity(cell_name=cell, path=results_path)
 evs_cell = spk[:, 0]
 sdf_mean_cell = []
 sdf_change_alltrials = []
-
+"""
 results_path_NO = f"/media/amtra/Samsung_T5/EBCC_NO_4Hz/"
 spk_NO = get_spike_activity(cell_name=cell, path=results_path_NO)
 evs_cell_NO = spk[:, 0]
 sdf_mean_cell_NO = []
 sdf_change_alltrials_NO = []
-
+"""
 # %% PLOT SDF
 palette = list(reversed(sns.color_palette("viridis", n_trials).as_hex()))
 sm = plt.cm.ScalarMappable(cmap="viridis_r", norm=plt.Normalize(vmin=0, vmax=n_trials))
@@ -48,12 +49,12 @@
     stop = CS_start_first + CS_burst_dur + trial * between_start
     sdf_cell = sdf(start=start, stop=stop, spk=spk, step=step)
     sdf_mean_cell.append(sdf_mean(sdf_cell))
-    sdf_cell_NO = sdf(start=start, stop=stop, spk=spk_NO, step=step)
-    sdf_mean_cell_NO.append(sdf_mean(sdf_cell_NO))
+    #sdf_cell_NO = sdf(start=start, stop=stop, spk=spk_NO, step=step)
+    #sdf_mean_cell_NO.append(sdf_mean(sdf_cell_NO))
 
 for trial in range(n_trials):
     axs_sdf[0].plot(sdf_mean_cell[trial], palette[trial])
-    axs_sdf[1].plot(sdf_mean_cell_NO[trial], palette[trial])
+    #axs_sdf[1].plot(sdf_mean_cell_NO[trial], palette[trial])
 
 axs_sdf[0].axvline(CS_start_first, label="CS start", linewidth=3, c=CS_color)
 axs_sdf[0].axvline(

compare_w-wo_NO/sdf_boxplots_048.py

@@ -11,8 +11,8 @@
 import scipy.stats as st
 
 noise_rates = [0, 4, 8]
-rooth_path = "/media/amtra/Samsung_T5/results/"
-with open("network_configuration.json", "r") as json_file:
+rooth_path = "/home/nomodel/code/NODS/results/"
+with open("/home/nomodel/code/NODS/network_configuration.json", "r") as json_file:
     net_config = json.load(json_file)
 
 CS_burst_dur = net_config["devices"]["CS"]["parameters"]["burst_dur"]
@@ -29,20 +29,20 @@
 
 cell = "pc_spikes"
 
-fig, axs = plt.subplots(1,3,figsize=(8,4))
+fig, axs = plt.subplots(3,1,figsize=(4,8))
 colors = [without_NO_color, with_NO_color, without_NO_color, with_NO_color]
 medianprops = dict(linewidth=1.5, color='white')
 positions = [1,2,4,5]
 
 for i, noise_rate in enumerate(noise_rates):
 
-    results_path = rooth_path + f"complete_EBCC/EBCC_{noise_rate}Hz/"
+    results_path = rooth_path + f"grid_search/{noise_rate}Hz/min4_plus8/6/"
     spk = get_spike_activity(cell_name=cell, path=results_path)
     sdf_mean_over_trials = []
     sdf_baseline = np.zeros((n_trials))
     sdf_cr = np.zeros((n_trials))
 
-    results_path_NO = rooth_path + f"complete_EBCC/EBCC_NO_{noise_rate}Hz/"
+    results_path_NO = rooth_path + f"grid_search/grid_NO/{noise_rate}Hz/0/"
     spk_NO = get_spike_activity(cell_name=cell, path=results_path_NO)
     sdf_mean_over_trials_NO = []
     sdf_baseline_NO = np.zeros((n_trials))
@@ -56,13 +56,13 @@
         sdf_cells = sdf(start=start, stop=stop, spk=spk, step=5)
         sdf_mean_trial = sdf_mean(sdf_cells)
         sdf_mean_over_trials.append(sdf_mean_trial)
-        sdf_baseline[trial] = np.mean(sdf_mean_trial[100:150])
+        sdf_baseline[trial] = np.mean(sdf_mean_trial[150:200])
         sdf_cr[trial] = np.mean(sdf_mean_trial[250:300])
 
         sdf_cells_NO = sdf(start=start, stop=stop, spk=spk_NO, step=5)
         sdf_mean_trial_NO = sdf_mean(sdf_cells_NO)
         sdf_mean_over_trials_NO.append(sdf_mean_trial_NO)
-        sdf_baseline_NO[trial] = np.mean(sdf_mean_trial_NO[100:150])
+        sdf_baseline_NO[trial] = np.mean(sdf_mean_trial_NO[150:200])
         sdf_cr_NO[trial] = np.mean(sdf_mean_trial_NO[250:300])
 
     sdf_change_baseline = sdf_baseline[1:] - sdf_baseline[1]
@@ -86,10 +86,11 @@
         patch.set_facecolor(color)
     axs[i].axvline(3,linewidth=1, color='black')
     axs[i].set_xticks([])
-    axs[i].set_ylim(-30,0)
+    axs[i].set_ylim(-25,5)
     axs[i].set_title(f'CS: 40Hz, BkG noise: {noise_rate}Hz')  
 
 plt.tight_layout()
-plt.show()
-fig.savefig(f"sdf_boxplots_048.png")
-fig.savefig(f"sdf_boxplots_048.svg")
+#plt.show()
+fig.savefig(f"sdf_boxplots_048_grid.png")
+#fig.savefig(f"sdf_boxplots_048.svg")
+# %%

demos/demo_single_pc_tree.py

@@ -438,17 +438,18 @@
             ID_cell = nest.GetStatus(spikedetector_GR, "events")[0]["senders"]
             active_sources = ID_cell[processed:]
             processed += len(active_sources)
-            sim.evaluate_diffusion(active_sources, t)
-            list_dict = []
-            for i in range(len(pfs)):
-                list_dict.append(
-                    {
-                        "meta_l": float(
-                            sig(x=sim.NO_in_ev_points[t, i], A=1, B=NO_threshold)
-                        )
-                    }
-                )
-            nest.SetStatus(pfs, list_dict)
+            if t % 2:
+                sim.evaluate_diffusion(active_sources, t)
+                list_dict = []
+                for i in range(len(pfs)):
+                    list_dict.append(
+                        {
+                            "meta_l": float(
+                                sig(x=sim.NO_in_ev_points[t, i], A=1, B=NO_threshold)
+                            )
+                        }
+                    )
+                nest.SetStatus(pfs, list_dict)
 t = time.time() - t0
 print("time {}".format(t))
 

grid_search_Aplus_Aminus/grid_search.py

@@ -0,0 +1,17 @@
+import sys
+import os
+import nest
+sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+from simulateEBCC import SimulateEBCC
+import numpy as np
+import gc
+
+n_sim = 10
+
+noise_rate = 4.0
+
+i = 4
+j = 1
+
+for k in range(0,n_sim):
+    os.system(f'python /home/nomodel/code/NODS/grid_search_Aplus_Aminus/simulation_grid.py {noise_rate} {k} {i} {j}')