diff --git a/doc/tutorials/unitary_event_analysis.ipynb b/doc/tutorials/unitary_event_analysis.ipynb
index 2b5c161f5..5ae16d1ad 100644
--- a/doc/tutorials/unitary_event_analysis.ipynb
+++ b/doc/tutorials/unitary_event_analysis.ipynb
@@ -46,6 +46,7 @@
     "\n",
     "import elephant.unitary_event_analysis as ue\n",
     "from elephant.datasets import download_datasets\n",
+    "from elephant.trials import TrialsFromBlock\n",
     "\n",
     "# Fix random seed to guarantee fixed output\n",
     "random.seed(1224)"
@@ -451,10 +452,7 @@
     "io = neo.io.NixIO(f\"{filepath}\",'ro')\n",
     "block = io.read_block()\n",
     "\n",
-    "spiketrains = []\n",
-    "# each segment contains a single trial\n",
-    "for ind in range(len(block.segments)):\n",
-    "    spiketrains.append (block.segments[ind].spiketrains)\n"
+    "spiketrains = TrialsFromBlock(block)\n"
    ]
   },
   {
@@ -473,19 +471,16 @@
     "UE = ue.jointJ_window_analysis(\n",
     "    spiketrains, bin_size=5*pq.ms, win_size=100*pq.ms, win_step=10*pq.ms, pattern_hash=[3])\n",
     "\n",
-    "plot_ue(spiketrains, UE, significance_level=0.05)\n",
-    "plt.show()"
+    "plot_ue([spiketrains.get_spiketrains_from_trial_as_list(idx) for idx in range(spiketrains.n_trials)], UE, significance_level=0.05)\n",
+    "plt.show()\n"
    ]
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "623e048a0474aa032839f97d38ba0837cc9041adc49a14b480c72f2df8ea99e3"
-  },
   "kernelspec": {
-   "display_name": "inm-elephant",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "inm-elephant"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -497,7 +492,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.12.5"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,