diff --git a/extras/benchmark/plot_isi_distributions.py b/extras/benchmark/plot_isi_distributions.py
new file mode 100644
index 000000000..ea8c3d10a
--- /dev/null
+++ b/extras/benchmark/plot_isi_distributions.py
@@ -0,0 +1,75 @@
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def read_isis_from_files(neuron_models):
+    data = {}
+    for neuron_model in neuron_models:
+        data[neuron_model] = {"isis": []}
+        iteration = 0
+        rank = 0
+        while True:
+            filename = "isi_distribution_[simulated_neuron=" + neuron_model + "]_[network_scale=20000]_[iteration=" + str(iteration) + "]_[nodes=2]_[rank=" + str(rank) + "]_isi_list.txt"
+            if not os.path.exists(filename):
+                break
+
+            with open(filename, 'r') as file:
+                isis = [float(line.strip()) for line in file]
+                data[neuron_model]["isis"].append(isis)
+
+            #iteration += 1
+            rank += 1
+
+    return data
+
+
+def analyze_data(data, bin_size):
+    # Determine the range for the bins
+    min_val = np.inf
+    max_val = -np.inf
+    for neuron_model in data.keys():
+        isi_list = data[neuron_model]["isis"]
+        for isi in isi_list:
+            min_val = min(min_val, min(isi))
+            max_val = max(max_val, max(isi))
+
+    bins = np.arange(min_val, max_val + bin_size, bin_size)
+
+    for neuron_model in data.keys():
+        isi_list = data[neuron_model]["isis"]
+        data[neuron_model]["counts"] = len(isi_list) * [None]
+        for i, isi in enumerate(isi_list):
+            counts, bin_edges = np.histogram(isi, bins=bins)
+            data[neuron_model]["counts"][i] = counts
+
+        data[neuron_model]["counts_mean"] = np.mean(np.array(data[neuron_model]["counts"]), axis=0)
+        data[neuron_model]["counts_std"] = np.std(np.array(data[neuron_model]["counts"]), axis=0)
+
+    data["bin_centers"] = (bin_edges[:-1] + bin_edges[1:]) / 2
+    data["min_val"] = min_val
+    data["max_val"] = max_val
+
+def plot_isi_distributions(neuron_models, data):
+    plt.figure(figsize=(10, 6))
+
+    for neuron_model in neuron_models:
+        plt.step(data["bin_centers"], data[neuron_model]["counts_mean"], label=neuron_model, linewidth=2, alpha=.5)
+        plt.errorbar(data["bin_centers"], data[neuron_model]["counts_mean"], yerr=data[neuron_model]["counts_std"], fmt='o', color='black', capsize=5, label='Variance')
+
+    plt.xlabel('ISI (ms)')
+    plt.ylabel('Frequency')
+    plt.title('ISI Distributions')
+    plt.legend()
+    plt.grid(True)
+    plt.show()
+
+# Example usage
+neuron_models = ["aeif_psc_alpha_neuron_Nestml_Plastic_noco__with_stdp_synapse_Nestml_Plastic_noco", "aeif_psc_alpha_neuron_Nestml_Plastic__with_stdp_synapse_Nestml_Plastic", "aeif_psc_alpha_neuron_Nestml", "aeif_psc_alpha"]
+
+bin_size = 5  # Adjust the bin size as needed
+
+data = read_isis_from_files(neuron_models)
+analyze_data(data, bin_size)
+plot_isi_distributions(neuron_models, data)
+